A Thread-Safe Arbitrary Precision Computation Package (Full

A Thread-Safe Arbitrary Precision Computation Package (Full
A Thread-Safe Arbitrary Precision
Computation Package
(Full Documentation)
David H. Bailey
∗
March 20, 2017
Abstract
Numerous research studies have arisen, particularly in the realm
of mathematical physics and experimental mathematics, that require
extremely high numeric precision. Such precision greatly magnifies
computer run times, so software packages to support high-precision
computing must be designed for thread-based parallel processing.
This paper describes a new package (“MPFUN2015”) that is threadsafe, even at the language interface level, yet still permits the working
precision level to be freely changed during execution. The package
comes in two versions: (a) a completely self-contained, all-Fortran
version that is simple to install; and (b) a version based on the MPFR
package (for lower-level operations) that is more complicated to install
but is approximately 3X faster. Except for a few special functions, the
two versions are “plug-compatible” in the sense that applications written for one also run with the other. Both versions employ advanced
algorithms, including FFT-based arithmetic, for optimal performance.
They also detect, and provide means to overcome, accuracy problems
rooted in the usage of inexact double-precision constants and expressions. A high-level Fortran-90 interface, supporting both multiprecision real and complex datatypes, is provided for each, so that most
users need only to make minor changes to existing code.
∗
Lawrence Berkeley National Laboratory (retired), 1 Cyclotron Road, Berkeley, CA
94720, USA, and University of California, Davis, Department of Computer Science. Email: david@davidhbailey.com.
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Contents
1 Applications of high-precision computation
1.1 The PSLQ integer relation algorithm . . . . . . .
1.2 High-precision numerical integration . . . . . . .
1.3 Ising integrals . . . . . . . . . . . . . . . . . . . .
1.4 Algebraic numbers in Poisson potential functions
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2 High-precision floating-point software
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2.1 Available software packages . . . . . . . . . . . . . . . . . . . 8
2.2 Thread safety . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 A thread-safe arbitrary precision computation package
3.1 Data structure . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 The MPFUN2015 solution to thread safety . . . . . . . . . . .
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4 Numerical algorithms used in MPFUN-Fort
4.1 Algorithms for basic arithmetic . . . . . . . . . .
4.2 FFT-based multiplication . . . . . . . . . . . . .
4.3 Advanced algorithm for division . . . . . . . . . .
4.4 Basic algorithms for transcendental functions . . .
4.5 Advanced algorithms for transcendental functions
4.6 Special functions . . . . . . . . . . . . . . . . . .
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5 Installation, compilation and linking
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6 Fortran coding instructions
6.1 Functions and subroutines . . . . . . . . . .
6.2 Input and output of multiprecision data . .
6.3 Handling double precision values . . . . . .
6.4 Dynamically changing the working precision
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7 Performance of sample applications
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7.1 Timings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
8 Conclusion
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2
1
Applications of high-precision computation
For many scientific calculations, particularly those that employ empirical
data, IEEE 32-bit floating-point arithmetic is sufficiently accurate, and is
preferred since it saves memory, run time and energy usage. For other applications, 64-bit floating-point arithmetic is required to produce results of
sufficient accuracy, although some users find that they can obtain satisfactory results by switching between 32-bit and 64-bit, using the latter only for
certain numerically sensitive sections of code. Software tools are being developed at the University of California, Berkeley and elsewhere to help users
determine which portions of a computation can be performed with lower
precision and which must be performed with higher precision [26].
Other applications, particularly in the fields of mathematical physics and
experimental mathematics, require even higher precision — tens, hundreds
or even thousands of digits. Here is a brief summary of these applications:
1. Supernova simulations (32–64 digits).
2. Optimization problems in biology and other fields (32–64 digits).
3. Coulomb n-body atomic system simulations (32–120 digits).
4. Electromagnetic scattering theory (32–100 digits).
5. The Taylor algorithm for ODEs (100–600 digits).
6. Ising integrals from mathematical physics (100–1000 digits).
7. Problems in experimental mathematics (100–50,000 digits and higher).
These applications are described in greater detail in [1, 3], which provides
detailed references. Here is a brief overview of a handful of these applications:
1.1
The PSLQ integer relation algorithm
Very high-precision floating-point arithmetic is now considered an indispensable tool in experimental mathematics and mathematical physics [1]. Many
of these computations involve variants of Ferguson’s PSLQ integer relation
detection algorithm [17, 8]. Suppose one is given an n-long vector (xi ) of real
3
or complex numbers (presented as a vector of high-precision values). The
PSLQ algorithm finds the integer coefficients (ai ), not all zero, such that
a1 x 1 + a2 x 2 + · · · + an x n = 0
(to available precision), or else determines that there is no such relation
within a certain bound on the size of the coefficients. Alternatively, one can
employ the Lenstra-Lenstra-Lovasz (LLL) lattice basis reduction algorithm
to find integer relations [22], or the “HJLS” algorithm, which is based on LLL.
Both PSLQ and HJLS can be viewed as schemes to compute the intersection
between a lattice and a vector subspace [14]. Whichever algorithm is used,
integer relation detection requires very high precision—at least (n × d)-digit
precision, where d is the size in digits of the largest ai and n is the vector
length, or else the true relation will be unrecoverable.
1.2
High-precision numerical integration
One of the most fruitful applications of the experimental methodology and
the PSLQ integer relation algorithm has been to identify classes of definite
integrals, based on very high-precision numerical values, in terms of simple
analytic expressions.
These studies typically employ either Gaussian quadrature or the “tanhsinh” quadrature scheme of Takahasi and Mori [27, 2]. The tanh-sinh quadrature algorithm approximates the integral of a function on (−1, 1) as
Z
1
f (x) dx ≈ h
−1
N
X
wj f (xj ),
(1)
j=−N
where the abscissas xj and weights wj are given by
xj = tanh (π/2 · sinh(hj))
wj = π/2 · cosh(hj)/ cosh (π/2 · sinh(hj))2 ,
(2)
and where N is chosen large enough that summation terms in (1) beyond N
(positive or negative) are smaller than the “epsilon” of the numeric precision
being used. Full details are given in [2]. An overview of applications of
high-precision integration in experimental mathematics is given in [4].
4
1.3
Ising integrals
In one study, tanh-sinh quadrature and PSLQ were employed to study the
following classes of integrals [7]. The Cn are connected to quantum field
theory, the Dn integrals arise in the Ising theory of mathematical physics,
while the En integrands are derived from Dn :
Z ∞
Z
1
du1
dun
4 ∞
···
···
Cn =
P
2
n! 0
u1
un
n
0
j=1 (uj + 1/uj )
Q ui −uj 2
Z ∞
Z ∞
i<j ui +uj
4
du1
dun
Dn =
···
···
2
P
n! 0
u1
un
n
0
j=1 (uj + 1/uj )
!2
Z 1
Z 1
Y uk − uj
En = 2
···
dt2 dt3 · · · dtn .
u
k + uj
0
0
1≤j<k≤n
Q
In the last line uk = ki=1 ti .
In general, it is very difficult to compute high-precision numerical values of
n-dimensional integrals such as these. But as it turn out, the Cn integrals can
be converted to one-dimensional integrals, which are amenable to evaluation
with the tanh-sinh scheme:
Z
2n ∞
Cn =
pK0n (p) dp.
n! 0
Here K0 is the modified Bessel function [24]. 1000-digit values of these sufficed to identify the first few instances of Cn in terms of well-known constants.
For example, C4 = 7ζ(3)/12, where ζ denotes the Riemann zeta function.
For larger n, it quickly became clear that the Cn approach the limit
lim Cn = 0.630473503374386796122040192710 . . . .
n→∞
This numerical value was quickly identified, using the Inverse Symbolic Calculator 2.0 (now available at http://carma-lx1.newcastle.edu.au:8087), as
lim Cn = 2e−2γ ,
n→∞
where γ is Euler’s constant. This identity was then proven [7].
5
Other specific results found in this study include the following:
D3
D4
E2
E3
E4
E5
8 + 4π 2 /3 − 27 L−3 (2)
4π 2 /9 − 1/6 − 7ζ(3)/2
6 − 8 log 2
10 − 2π 2 − 8 log 2 + 32 log2 2
22 − 82ζ(3) − 24 log 2 + 176 log2 2 − 256(log3 2)/3
+16π 2 log 2 − 22π 2 /3
= 42 − 1984 Li4 (1/2) + 189π 4 /10 − 74ζ(3) − 1272ζ(3) log 2 + 40π 2 log2 2
−62π 2 /3 + 40(π 2 log 2)/3 + 88 log4 2 + 464 log2 2 − 40 log 2,
=
=
=
=
=
where ζ is the Riemann zeta function and Lin (x) is the polylog function.
E5 was computed by first reducing it to a 3-D integral of a 60-line integrand, which was evaluated using tanh-sinh quadrature to 250-digit arithmetic using over 1000 CPU-hours on a highly parallel system. The PSLQ
calculation required only seconds to produce the relation above. This formula
remained a “numerical conjecture” for several years, but was proven in March
2014 by Erik Panzer, who mentioned that he relied on these computational
results to guide his research.
1.4
Algebraic numbers in Poisson potential functions
The Poisson equation arises in contexts such as engineering applications,
the analysis of crystal structures, and even the sharpening of photographic
images. In two recent studies [5, 6], the present author and others explored
the following class of sums:
φn (r1 , . . . , rn ) =
1
π2
X
m1 ,...,mn
eiπ(m1 r1 +···+mn rn )
.
m21 + · · · + m2n
odd
(3)
After extensive high-precision numerical experimentation using (??), we discovered (then proved) the remarkable fact that when x and y are rational,
φ2 (x, y) =
1
log A,
π
(4)
where A is an algebraic number, namely the root of an algebraic equation
with integer coefficients.
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In our experiments we computed α = A8 = exp(8πφ2 (x, y)), using some
rapidly convergent formulas found in [5], for various simple rationals x and
y (as it turns out, computing A8 reduces the degree of polynomials and
so computational cost). Then we generated the vector (1, α, α2 , · · · , αd ) as
input to a program implementing the three-level multipair PSLQ program
[8]. When successful, the program returned the vector of integer coefficients
(a0 , a1 , a2 , · · · , ad ) of a polynomial satisfied by α as output. With some experimentation on the degree d, and after symbolic verification using Mathematica, we were able to ensure that the resulting polynomial is in fact the
minimal polynomial satisfied by α. Table 1 shows some examples [5].
s
5
6
7
8
9
10
Minimal polynomial corresponding to x = y = 1/s:
1 + 52α − 26α2 − 12α3 + α4
1 − 28α + 6α2 − 28α3 + α4
−1 − 196α + 1302α2 − 14756α3 + 15673α4 + 42168α5 − 111916α6 + 82264α7
−35231α8 + 19852α9 − 2954α10 − 308α11 + 7α12
1 − 88α + 92α2 − 872α3 + 1990α4 − 872α5 + 92α6 − 88α7 + α8
−1 − 534α + 10923α2 − 342864α3 + 2304684α4 − 7820712α5 + 13729068α6
−22321584α7 + 39775986α8 − 44431044α9 + 19899882α10 + 3546576α11
−8458020α12 + 4009176α13 − 273348α14 + 121392α15
−11385α16 − 342α17 + 3α18
1 − 216α + 860α2 − 744α3 + 454α4 − 744α5 + 860α6 − 216α7 + α8
Table 1: Sample of polynomials produced in earlier study [5].
Using this data, Jason Kimberley of the University of Newcastle, Australia, conjectured a formula that gives the degree d as a function of k [5].
These computations required prodigiously high precision and correspondingly
long run times. For example, finding the degree-128 polynomial satisfied by
α = exp(8πφ2 (1/32, 1/32)) required 10,000-digit precision. Other runs were
attempted, but failed.
With these prodigiously high numeric precision levels and huge run times,
it is clear that a highly parallel PSLQ facility, together with a parallelizable
(at the application level) arbitrary precision facility, is badly needed for further research. But attempts to exploit this parallelism for such problems to
date have been largely hamstrung by the fact that most available arbitrary
precision software packages are not thread-safe.
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2
High-precision floating-point software
By far the most common form of extra-precision arithmetic is roughly twice
the level of standard 64-bit IEEE floating-point arithmetic. One option is the
IEEE standard for 128-bit binary floating-point arithmetic, with 113 mantissa bits, but sadly it is not yet widely implemented in hardware, although
it is supported, in software, in some compilers.
Another software option for this level of precision is “double-double”
arithmetic (approximately 31-digit accuracy). This datatype consists of a
pair of 64-bit IEEE floats (s, t), where s is the 64-bit floating-point value
closest to the desired value, and t is the difference (positive or negative)
between the true value and s. One can extend this design to quad-double
arithmetic, which operates on strings of four IEEE 64-bit floats, providing
roughly 62-digit accuracy. These two datatypes are supported by the QD
package, which includes high-level language interfaces for C++ and Fortran
(see below) [21].
For higher-levels of precision, software packages typically represent a highprecision datum as a string of floats or integers, where the first few words
contain bookkeeping information and the binary exponent, and subsequent
words (except perhaps near the end) contain the mantissa. For moderate precision levels (up to roughly 1000 digits), arithmetic on such data is typically
performed using adaptations of familiar schemes.
Above about 1000 or 2000 decimal digits, advanced algorithms should be
employed for maximum efficiency. For example, a high-precision multiply operation can be performed by noting that the key operation is merely a linear
convolution, which may be performed using fast Fourier transforms (FFTs).
Efficient implementations of this scheme can dramatically accelerate multiplication, since the FFT reduces an O(n2 ) operation to an O(n log n log log n)
operation [13, Section 2.3] (see also Section 4.2).
2.1
Available software packages
Software for performing high-precision arithmetic has been available for quite
some time, for example in the commercial packages Mathematica and Maple.
However, until 10 or 15 years ago, those with applications written in more
conventional languages, such as C++ or Fortran-90, often found it necessary
to rewrite their codes, replacing each arithmetic operation with a subroutine
call, which was a very tedious and error-prone process. Nowadays there
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are several freely available high-precision software packages, together with
accompanying high-level language interfaces, utilizing operator overloading,
that make code conversions relatively painless.
Here are some packages for high-precision floating-point computation:
• ARPREC: supports arbitrary precision real, integer and complex, with
many algebraic and transcendental functions. Includes high-level interfaces for C++ and Fortran-90. Available at http://crd-legacy.
lbl.gov/~dhbailey/mpdist.
• CLN: C++ library supporting arbitrary precision integer, real and complex, with numerous algebraic and transcendental functions. Available
at http://www.ginac.de/CLN.
• GMP: supports high-precision integer, rational and floating-point calculations. Distributed under the GNU license by the Free Software
Foundation. Available at http://gmplib.org.
• Julia: high-level programming environment that incorporates GMP
and MPFR. Available at http://julialang.org.
• MPFR: supports multiple-precision floating-point computations with
correct rounding, based on GMP. Includes numerous algebraic and
transcendental functions, and a thread-safe build option. Available
at http://www.mpfr.org.
• MPFR++: a high-level C++ interface to MPFR (although the currently available version is not up-to-date with MPFR). Available at
http://perso.ens-lyon.fr/nathalie.revol/software.html.
• MPFR C++: a high-level C++ interface to MPFR with a threadsafe option. See Section 3.3 for some additional details. Available at
http://www.holoborodko.com/pavel/mpfr.
• MPFUN90: similar to ARPREC in user-level functionality, but written entirely in Fortran-90. Includes a Fortran-90 high-level interface.
Available at http://crd-legacy.lbl.gov/~dhbailey/mpdist.
• mpmath: a Python library for arbitrary precision floating-point arithmetic, including numerous transcendentals. Available at https://
code.google.com/p/mpmath.
9
• NTL: a C++ library for arbitrary precision integer and floating-point
arithmetic. Available at http://www.shoup.net/ntl.
• Pari/GP: a computer algebra system that includes facilities for highprecision arithmetic, with many transcendental functions. Available at
http://pari.math.u-bordeaux.fr.
• QD: includes routines to perform “double-double” (approx. 31 digits)
and “quad-double” (approx. 62 digits) arithmetic, as well as many
algebraic and transcendental functions. Includes high-level interfaces
for C++ and Fortran-90. Available at http://crd-legacy.lbl.gov/
~dhbailey/mpdist.
• Sage: an open-source symbolic computing system that includes highprecision facilities. Available at http://www.sagemath.org.
2.2
Thread safety
The scientific computing world is moving rapidly into multicore and multinode parallel computing, because the frequency and performance of individual processors is no longer rapidly increasing [28]. Thus it is clear that future
improvements in performance on high-precision computations will only be
obtained by aggressively exploiting parallelism. It is difficult to achieve significant parallel speedup within a single high-precision arithmetic operation,
but parallelization at the application level, e.g., parallelizing a DO or FOR
loop in an application code, is an attractive option.
It is possible to perform some high-precision computations in parallel by
utilizing message passing interface (MPI) software at the application level.
MPI employs a “shared none” environment that avoids many difficulties.
Indeed, several high-precision applications have been performed on highly
parallel systems using MPI, including the study mentioned in Section 1.3 [7].
But on modern systems that feature multicore processors, parallel computing is more efficiently performed using a shared memory, multithreaded
environment such as OpenMP [28] within a single node, even if MPI is
employed for parallelism between nodes. Furthermore, algorithms such as
PSLQ, for example, can only be parallelized efficiently at a rather low loop
level — MPI implementations are not effective here unless the precision level
is exceedingly high.
10
Computations that use a thread-parallel environment such as OpenMP
must be entirely “thread-safe,” which means, among other things, that there
is no read/write global data, because otherwise there may be difficulties
with processors stepping on each other during parallel execution. Employing
“locks” and the like may remedy such difficulties, but this reduces parallel
efficiency and is problematic for code portability and installation.
One impediment to thread safety is the design of the operator overloading
feature of modern computer languages, which is the only reasonable way to
program a complicated high-precision calculation. Here “operator overloading” means the feature, available in several high-level languages including
C++ and Fortran-90, where algebraic operators, e.g., +, -, * and /, are extended to high-precision operands. Such facilities typically do not permit
one to carry information such as the current working precision level.
Most arbitrary precision packages generate a “context” of auxiliary data,
such as the current working precision level and data to support transcendental function evaluation. Such data, if not handled properly, can ruin thread
safety. For most high-precision computation packages, the available documentation makes no statement one way or the other (which almost certainly
means that they are not thread-safe).
Of the packages listed in Section 2, only one is a certified thread-safe,
high-level floating-point package (i.e., uses operator overloading to interface
with ordinary source code), namely the MPFR C++ package [23], which
is built upon the lower-level MPFR package [19]. The MPFR package in
turn is very well-designed, features correct rounding to the last bit, includes
numerous transcendental and special functions, and achieves the the fastest
overall timings of any floating-point package in the above list [15].
According to the documentation, the MPFR package has a “thread-safe
build option.” When this is invoked, the package generates a context, local to
the thread, to support computation of certain transcendental functions at a
particular precision level, whenever a high-precision operation is initiated in
that thread. This is not ideal, since it means that if one is using thread-based
parallelism to parallelize a low-level loop, this context must be generated at
the start of each thread and freed at the end of the thread. However, one of
the developers of MPFR has promised to the present author that in a future
version, this context can be generated in a one-time initialization, after which
all computation will be thread-safe.
There is, to this author’s knowledge, no high-level, thread-safe arbitrary
precision package to support Fortran applications, prior to the present work.
11
While thread safety is of paramount importance, several other lessons
should be noted when designing a high-precision floating-point arithmetic
facility, based on the present author’s experience:
• Double precision constants and expressions pose a serious problem,
since they are not automatically converted to high precision due to
operator precedence rules in most programming languages. A high
percentage of accuracy failures reported to the present author by users
of his earlier packages (ARPREC, MPFUN90 and QD) are due to the
usage of inexact double precision values.
• Complicated installation scripts are problematic. Many support inquiries for the author’s earlier packages have been for installation issues, not the underlying multiprecision software. Some users prefer
older, out-of-date software, simply because it is easy to install.
• Special system settings, system-dependent features and exotic language
features pose serious difficulties for portability and maintenance.
• As with any software activity, long-term support is a nagging issue.
3
A thread-safe arbitrary precision computation package
With this background, the present author has developed a new software
package for arbitrary-precision computation, named “MPFUN2015.” It is
available in two versions: (a) MPFUN-Fort, a completely self-contained, allFortran version that is simple to install; and (b) MPFUN-MPFR, a version
based on the MPFR package for lower-level operations that is more complicated to install but is approximately 3X faster for most applications. Except
for a few special functions, the two versions are “plug-compatible” in the
sense that applications written for one will also run with the other without
any changes (see Section 6.4 for details). These versions feature:
1. Full support for both real and complex datatypes, including all basic
arithmetic operations and transcendental functions. A full-featured
high-level language interface for Fortran-90 is provided, so that most
users need only make minor changes to existing double precision code.
A C++ interface is planned but is not yet written.
12
2. A 100% thread safe design, even at the user language interface level,
since no “context” or auxiliary data needs to be generated (unless exceedingly high precision is used). The working precision can be freely
changed (up to a flexible limit) during execution, even in low-level
loops, without ruining thread safety. See however Section 3.3.
3. Numerous transcendental functions and special functions, including
most of the intrinsic funtions specified in the Fortran-2008 standard.
See Section 6.1 for a complete listing.
4. There is no need to manually allocate or deallocate temporary multiprecision variables or arrays in subroutines — all such data works with
the standard automatic array feature of Fortran, and is thread-safe.
5. No system-dependent features or settings. Proper operation does not
depend on the IEEE rounding mode of the user application (although
see item 11 below).
6. Straightforward installation, using scripts provided for various environments. MPFUN-Fort is supported with the GNU, IBM, Intel and
Portland Group Fortran compilers, and MPFUN-MPFR is supported
on the GNU and Intel Fortran compilers, provided that the gcc compiler is also available for installation of GMP and MPFR.
7. Precision is theoretically scalable to millions of decimal digits, although
at present the MPFUN-Fort version is limited to 1.2 million digits.
8. Advanced algorithms, including FFT-based arithmetic, are employed
in both versions for top performance even at very high precision levels.
9. A highly effective solution is provided to the double precision accuracy
problem mentioned in Section 2.2.
10. The overall runtime performance of the MPFUN-Fort version is somewhat faster than the present author’s ARPREC package; the MPFUNMPFR version is roughly 3X faster than either on large problems.
11. With the MPFUN-MPFR version, computations are guaranteed accurate to the last bit. However both versions perform computations to
one more word (14–19 digits) of precision than requested by the user,
minimizing roundoff error.
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3.1
Data structure
For the MPFUN-Fort version, the structure is a (N + 6)-long vector of 64-bit
IEEE floating-point words, where N is the number of manttisa words:
• Word 0: Total space allocated for this array, in 64-bit words.
• Word 1: The working precision level (in words) associated with this
data.
• Word 2: The number of mantissa words N ; the sign of word 2 is the
sign of the value.
• Word 3: The multiprecision exponent, base 248 .
• Word 4 to N + 3: N mantissa words (whole numbers between 0 and
248 − 1).
• Word N + 4 and N + 5: Scratch words for internal usage.
For the MPFUN-MPFR version, the structure is a (N + 6)-long vector of
64-bit integers:
• Word 0: Total space allocated for this array, in 64-bit words.
• Word 1: The working precision level (in bits) associated with this data.
• Word 2: The sign of the value.
• Word 3: The exponent, base 2.
• Word 4: A pointer to the first word of the mantissa, which in MPFUNMPFR always points to Word 5.
• Word 5 to N + 4: Mantissa words (unsigned integers between 0 and
264 − 1).
• N + 5: Not used at present.
Note that in the MPFUN-MPFR version, words 1 through N + 4 correspond
exactly to the data structure of the MPFR package.
For both versions, a complex multiprecision datatype is a contiguous pair
of real multiprecision data. Note that the imaginary member of the realimaginary pair starts at an offset in the array equal to the value of word 0.
Note also that this offset is independent of the working precision.
14
3.2
Modules
The MPFUN-Fort software includes the following separate modules, each in
its own source file:
1. MPFUNA: Contains compile-time global data. In the MPFUN-Fort
version, this module includes data √
to support FFT-based arithmetic
and binary values of log 2, π and 1/ 2 (up to 19,500-digit precision).
2. MPFUNB (present only in MPFUN-Fort): Handles basic arithmetic
functions, rounding, normalization, square roots and n-th roots. The
FFT-based arithmetic facility to support very high precision computation is included in this module.
3. MPFUNC (present only in MPFUN-Fort): Handles binary-to-decimal
conversion, decimal-to-binary conversion and input/output operations.
4. MPFUND (present only in MPFUN-Fort): Includes routines for all
common transcendental constants and functions, as well as special routines, implementing advanced algorithms, for very high precision levels.
5. MPFUNE (present only in MPFUN-Fort): Includes routines for special
functions, such as the BesselJ, gamma, incomplete gamma and zeta
functions.
6. MPFUNF: Defines the default (maximum) precision. This is the only
module that needs to be modified by the user.
7. MPFUNG: A high-level user interface that connects to user code via
operator overloading and function extension.
8. MPMODULE: The main module that references the others and is referenced by the user.
3.3
The MPFUN2015 solution to thread safety
All of the software modules above are 100% thread safe. There are no global
parameters or arrays, except for static, compile-time data, and no initialization is required unless extremely high precision is required. For both the
MPFUN-Fort and MPFUN-MPFR versions, working precision level is passed
as a subroutine argument, ensuring thread safety. For the MPFUN-MPFR
15
version, for the time being thread safety cannot be guaranteed unless the
user’s code does not involve transcendental functions in multi-threaded sections of code. This limitation will be removed in a future release.
Thread safety at the language interface or user level in both versions is
achieved by assigning a working precision level to each multiprecision datum,
which then is passed through the multiprecision software. Note, in the data
structure for both MPFUN-Fort and MPFUN-MPFR given in Section 3.1
above, that word 1 (the second word of the array) is the working precision
level associated with that datum. This solves the thread safety problem when
precision is dynamically changed in the application, although it requires a
somewhat different programming style, as we shall briefly explain here (see
Section 6.4 for additional details).
To use either version from a Fortran program, the user first sets the parameter mpipl, the “default” precision level in digits, which is the maximum
precision level to be used for subsequent computation, and is used to set the
amount of storage required for multiprecision data. mpipl is set in a parameter statement at the start of module MPFUNF, which is in file mpfunf.f90.
In the code as distributed, mpipl is set to 1200 digits (sufficient to run the
six test problems of Section 7), but it can be set to any level greater than or
equal to 30 digits. mpipl is converted to mantissa words (parameter mpwds),
which is the internal default precision level.
All computations are performed to mpwds words precision unless the user,
in an application code, specifies a lower value. During execution, the user can
change the working precision assigned to a multiprecision variable or array by
using the built-in functions mpreal and mpcmplx (see Section 6.4 for details).
The working precision level assigned to a particular multiprecision variable
or array element can be monitored using the built-in function mpwprec.
During execution, the result of any operation involving multiprecision
variables or array elements “inherits” the working precision level of the input
operands (if the operands have different working precision levels, the higher
precision level is chosen for the result). When assigning a multiprecision
variable or array element to double precision constant or variable, or when
reading multiprecision data from a file, the result is assigned the default
precision unless a lower precision level is specified.
16
4
Numerical algorithms used in MPFUN-Fort
As mentioned above, MPFUN-MPFR relies on MPFR for all lower-level
arithmetic operations and transcendental functions, whereas MPFUN-Fort is
completely self-contained. The algorithms employed in the MPFR package
are described in [19]. This section presents a brief overview of the algorithms
used in MPFUN-Fort. Those readers primarily interested in MPFUN-MPFR
may skip to Section 5.
4.1
Algorithms for basic arithmetic
Multiplication. For modest levels of precision, MPFUN-Fort employs adaptations of the usual schemes we all learned in grade school, where the number
base is 248 = 281474976710656 instead of ten. In the inner loop of the multiplication routine division routines, note that two such numbers a and b in
the range [0, 248 ) must be multiplied, obtaining the exact 96-bit result.
This is performed in MPFUN-Fort by splitting both a and b into high- and
low-order 24-bit parts, using the sequence of operations a1 = 224 int (2−24 a),
a2 = a−a1 . Note that a = a1 +a2 . If the four half-sized values are a1 , a2 , b1 , b2 ,
then calculate c = a1 ·b2 +a2 ·b1 , then c1 = 248 int 2−48 (c), c2 = c−c1 . Finally
calculate d1 = 2−48 (a1 · b1 + c1 ), d2 = a2 · b2 + c2 . Then d1 and d2 are the highand low-order 48-bit mantissa words of the product, with the proviso that
although d = d1 + d2 = a · b is mathematically correct, d1 and d2 might not
be the precisely correct split into two words in the range [0, 248 ). However,
since a double precision datatype can exactly hold whole numbers up to 253
in size, no accuracy is lost. In fact, this operation can be repeatedly done in
a loop, provided that this data is periodically corrected in a normalization
operation. Note also that the splitting of data in at least one of the two
arguments at the beginning can be done outside the inner loop.
The resulting scheme is very efficient yet totally reliable — in effect, it
performs quad precision with only a handful of register-level operations. Note
also that if two n-word arguments are multiplied, and the working precision
is also n words, then since only an n-word result is returned, only slightly
more than half of the “multiplication pyramid” need be calculated.
Division. A similar approach can be taken to division. Again, the key step is
to inexpensively simulate quad precision in the inner loop, using the scheme
outlined above.
17
Square roots. Square roots are calculated
by the following Newton-Raphson
√
iteration, which converges to 1/ a [10, pg. 227]:
xk+1 = xk + 1/2 · (1 − x2k · a) · xk ,
(5)
where the multiplication () · xk is performed with only half of the normal
level of precision. These iterations are performed with a working precision
level that approximately doubles with each iteration, except that at three
iterations before the final iteration, the iteration is repeated without doubling
the precision, in order to enhance accuracy. The final iteration is performed
as follows (due to A. Karp):
√
a ≈ (a · xn ) + 1/2 · [a − (a · xn )2 ] · xn ,
(6)
where the multiplications (a · xn ) and [] · xn are performed with only half the
final level of precision. If this is done properly, the total cost of the calculation
is only about three times the cost of a single full-precision multiplication.
n-th roots. A similar scheme is used to compute n-th roots for any integer
n. Computing xnk , which is required here, can be efficiently performed using
the binary algorithm for exponentiation. This is merely the observation that
exponentiations can be accelerated based on the binary expansion of the
exponent: for example, 317 can be computed as ((((3)2 )2 )2 )2 · 3 = 129140163.
Note that these algorithms are trivially thread-safe, since no auxiliary
data is involved.
4.2
FFT-based multiplication
Although the multiplication algorithm described above is very efficient, for
higher levels of precision (above approximately 2500 digits, based on the
present author’s implementation), significantly faster performance can be
achieved by employing an FFT-convolution approach [13][10, pg. 223–224].
Suppose one wishes to multiply two n-precision values whose mantissa
words are given by a = (a0 , a1 , a2 , · · · , an−1 ) and b = (b0 , b1 , b2 , · · · , bn−1 ). It
is easy to see that the desired result, except for releasing carries, is an acyclic
convolution. In particular, assume that a and b are extended to 2n words
each by padding with zeroes. Then the product c = (ck ) is given by
ck =
2n−1
X
aj bk−j ,
j=0
18
0 ≤ k < 2n,
(7)
where bk−j is read as bk−j+2n when k − j is negative. This convolution can be
calculated as (c) = F −1 [F (a) · F (b)], where F (a) and F (b) denote a real-tocomplex discrete Fourier transform (computed using an FFT algorithm), the
dot means element-by-element complex multiplication, and F −1 [] means an
inverse complex-to-real FFT. The ck results from this process are floatingpoint numbers. Rounding these values to the nearest integer, and then releasing carries beginning at c2n−1 gives the desired multiplication result.
The FFT-based multiplication facility of the present MPFUN-Fort software supports a precision level up to approximately 1.2 million digits. Beyond this level, numerical round-off error in the FFT is too great to reliably
round the ck results to the nearest integer. If the maximum rounding error
exceeds 0.375 (beyond which is deemed unsafe), an error message is output. A planned future enhancement to MPFUN-Fort will extend the usable
precision level to at least 100 million decimal digits.
In contrast to the basic arithmetic algorithms, FFT-based multiplication
requires precomputed FFT root-of-unity data. However, all the requisite
FFT data to support any precision level up to 19,500 digits is stored as
static data in module MPFUNA. Thus up to this level, MPFUN-Fort requires
no initialization, and is completely thread-safe (since no “context” must be
created or freed). If even higher precision is required, the requisite FFT data
is generated by calling mpinit — see Table 4 and Section 6.4 for details —
after which all computations are completely thread-safe.
4.3
Advanced algorithm for division
With an FFT-based multiplication facility in hand, division of two extrahigh-precision arguments a and b can be performed by the following scheme.
This Newton-Raphson algorithm iteration converges to 1/b [10, pg. 226]:
xk+1 = xk + (1 − xk · b) · xk ,
(8)
where the multiplication () · xk is performed with only half of the normal
level of precision. These iterations are performed with a working precision
level that is approximately doubles with each iteration, except that at three
iterations before the final iteration, the iteration is repeated without doubling
the precision, in order to enhance accuracy. The final iteration is performed
as follows (due to A. Karp):
a/b ≈ (a · xn ) + [a − (a · xn ) · b] · xn ,
19
(9)
where the multiplications a · xn and [] · xn are performed with only half of the
final level of precision. The total cost of this procedure is only about three
times the cost of a single full-precision multiplication.
4.4
Basic algorithms for transcendental functions
Most arbitrary precision packages require a significant “context” of data to
support transcendental function evaluation at a particular precision level,
and this data is often problematic for both thread safety and efficiency. For
example, if this context data must be created and freed within each running
thread, this limits the efficiency in a multithreaded environment. With this
in mind, the transcendental function routines in MPFUN-Fort were designed
to require only a minimum of context, which context is provided in static
data statements, except when extremely high precision is required.
Exponential and logarithm. In the current implementation, the exponential
function routine in MPFUN-Fort first reduces the input argument to within
the interval (− log(2)/2, log(2)/2]. Then it divides this value by 2q , producing
a very small value, which is then input to the Taylor series for exp(x). The
working precision used to calculate the terms of the Taylor series is reduced
as the terms get smaller, thus saving approximately one-half of the total run
time. When complete, the result is squared q times, and then corrected for
the initial reduction. In the current implementation, q is set to the nearest
integer to (48n)2/5 , where n is the number of words of precision.
Since the Taylor series for the logarithm function converges much more
slowly than that of the exponential function, the Taylor series is not used for
logarithms unless the argument is extremely close to one. Instead, logarithms
are computed based on the exponential function, by employing the following
Newton iteration with a level of precision that approximately doubles with
each iteration:
xk = xk −
ex − a
.
ex
(10)
Trigonometric functions. The sine routine first reduces the input argument
to within the interval (−π, π], and then to the nearest octant (i.e., the nearest
multiple of π/4). This value is then divided by 2q , producing a small value,
which is then input to the Taylor series for sin(x), with a linearly
varying
p
precision level as above. When complete, cos(x) is computed as 1 − sin2 (x),
20
and then the double-angle formula
cos(2x) = 2 cos2 (x) − 1,
(11)
is applied q√ times. In the current implementation, q is set to the nearest
integer to 24n − 3, unless the reduced argument is very close to one, in
which case q = 0. After applying the double-angle formula, the result is then
adjusted for its octant.
For example, if x is closest to √
π/4, so that x = t+π/4,
√
then cos(x) = 1/ 2(cos(t) − sin(t)) and sin(x) = 1/ 2(cos(t) + sin(t)).
The inverse cos/sin function is based on the sine routine, by employing a
Newton iteration with a level of numeric precision that roughly doubles with
each iteration.
Power function. The power function, namely ab for real a > 0 and b, can be
computed as eb log a . To further accelerate this operation, the MPFUN-Fort
software first examines the value of b to see if it is a rational number with
numerator and denominator up to 107 size, using the extended Euclidean
algorithm performed in double precision. If it is, ab is performed using a
combination of the binary algorithm for exponentiation for the numerator,
and the n-th root function for the denominator.
Note that for all of the above algorithms,
the only context required is
√
the numerical values of log(2), π and 1/ 2. For precision levels up to approximately 19,500 digits, these values are stored as static data in module
MPFUNA. If higher precision is required, this data is generated by calling
subroutine mpinit — see Table 4 and Section 6.4 for details.
4.5
Advanced algorithms for transcendental functions
The above transcendental function schemes are adequate for modest levels of
precision. For higher levels of precision, advanced, quadratically convergent
algorithms are employed.
Logarithm and exponential. To compute logarithms for higher levels of precision, MPFUN-Fort employs an algorithm due to Richard Brent [12]: Inputs
t that are extremely close to 1 are handled using a Taylor series. Otherwise,
let n be the number of bits of precision required in the result. If t is exactly
two, select m > n/2. Then the following formula gives log(2) to the required
precision:
π
.
(12)
log(2) =
2mA(1, 4/2m )
21
Here A(a, b) is the limit of the arithmetic-geometric mean (AGM): Let a0 = a
and b0 = b; then iterate
p
ak b k
(13)
ak+1 = (ak + bk )/2,
bk+1 =
until convergence. For other t, select m such that s = t2m > 2n/2 . Then the
following formula gives log t to the required precision:
log(t) =
π
− m log(2).
2A(1, 4/s)
(14)
Given this algorithm for logarithms, high-precision exponentials can be calculated using the following Newton iteration, using a precision level that
approximately doubles with each iteration as before:
xk+1 = xk − xk (log xk − a).
(15)
Trigonometric functions. Trigonometric functions and their inverses can be
calculated by applying the above algorithms for a complex argument and
recalling Euler’s formula
er+iθ = er (cos(θ) + i sin(θ)).
(16)
The complex logarithm thus gives the inverse trigonometric functions. In
particular, given the complex argument x + iy, the complex logarithm gives
r and θ (with θ in the interval (−π, π]) such that r cos(θ) = x and r sin(θ) = y.
The complex exponential function, which gives cos and sin, can be computed
from the complex logarithm function using Newton iterations as before.
Along this line, π itself can be computed using√an AGM-based
√ scheme
due to Brent and Salamin [12]. Set a0 = 1, b0 = 1/ 2, and d0 = 2 − 1/2.
Then iterate the following starting with k = 1 until convergence: with k = 1:
ak = (ak−1 + bk−1 )/2
p
bk = ak−1 bk−1
dk = dk−1 − 2k (ak − bk )2 .
(17)
Then pk = (ak + bk )2 /dk converges to π.
Each of these advanced algorithms, which are based on the AGM, is
“quadratically convergent” — successive iterations approximately doubles
the number of correct digits.
22
Based on the present author’s implementation, the advanced exponential function algorithm is faster than the conventional routine beginning at
about 5,800 digits. However, the advanced logarithm scheme is faster than
the conventional logarithm after only 430 digits. Sadly, although the advanced trigonometric function algorithm is not faster until above approximately 1,000,000 digits, the advanced inverse trigonometric routine is faster
after only 1,500 digits.
Note that none of these algorithms requires any context, except for the
numerical values of log 2 and π, which, as noted above, are stored in the
program code itself for precision levels up to 19,500 digits.
4.6
Special functions
Modern mathematical and scientific computing frequency often involves other,
more sophisticated functions, which collectively are termed “special functions” [16]. A number of these functions are planned for future inclusion in
MPFUN-Fort, and will be added to the version as they are developed. Here
is a brief description of the functions that have been implemented and the
algorithms employed. In each case, care is taken to preserve thread safety,
and to avoid, as far as possible, any need to precalculate auxiliary data.
BesselJ function. The BesselJ function, or, more formally, the Bessel function
of the first kind, is defined as [16, 10.2.2]:
Jν (z) =
∞
z ν X
(−1)k
2
k=0
2 k
z
4
k!Γ(ν + k + 1)
.
(18)
For modest-sized values of z (the present author uses the condition z <
1.73d, where d is the precision level in digits), the MPFUN-Fort BesselJ
function implements formula (18). Note that although (18) involves the
gamma function (see below), this is only called once to compute Γ(ν + 1),
after which the recursion Γ(t + 1) = tΓ(t) can be applied for the other terms.
For larger values of z, the following asymptotic formula is employed [16,
10.17.3]. Define ω = z − νπ/2 − π/4, and
ak (ν) =
(4ν 2 − 12 )(4ν 2 − 32 ) · · · (4ν 2 − (2k − 1)2 )
.
k!8k
23
(19)
Then
Jν (z) =
2
πz
1/2
cos ω
∞
X
(−1)k a2k (ν)
k=0
z2k
− sin ω
∞
X
(−1)k a2k+1 (ν)
k=0
z 2k+1
!
.
(20)
One important detail omitted from the above discussion is that large
amounts of cancellation occurs in these formulas. Thus when evaluating these
formulas, a working precision of 1.5 times the normal working precision is
employed.
No precalculated auxiliary data is needed for either of these algorithms,
so they are thread safe.
Gamma function. The gamma function employs a very efficient but littleknown formula due to Ronald W. Potter [25], as follows. If the input t is a
positive integer, then Γ(t) = (t−1)!. If not, use the recursion Γ(t+1) = tΓ(t)
to reduce the argument (positive or negative) to the interval (0, 1). Then
define α = nint (n/2 · log 2), where n is the number of bits of precision and
nint means nearest integer, and set z = α2 /4. Define the Pochhammer
function as
(ν)k = ν(ν + 1)(ν + 2) · · · (ν + k − 1).
(21)
Then define the functions
∞
z ν X
(z 2 /4)k
ν
2
k!(ν)k+1
k=0
∞
z −ν
X
(z 2 /4)k
B(ν, z) =
(−ν)
.
2
k!(−ν)
k+1
k=0
A(ν, z) =
With these definitions, the gamma function can then be computed as
s
A(ν, z)
π
Γ(ν) =
.
B(ν, z) ν sin(πν)
(22)
(23)
No auxiliary data is needed for this algorithm, so it is thread-safe.
Incomplete gamma function. For modest-sized positive arguments (the author uses the condition z < 2.768d, where d is the precision level in digits),
24
the MPFUN-Fort incomplete gamma function is evaluated using the following formula [16, 8.7.3]:
!
∞
zk
za X
.
(24)
Γ(a, z) = Γ(a) 1 − z
e k=0 Γ(a + k + 1)
Note, as with the BesselJ function, that although formula (24) involves the
gamma function, this is only called once to compute Γ(a + 1), after which
the recursion Γ(t + 1) = tΓ(t) can be applied for all other terms.
For large values of z, the following asymptotic formula is used [16, 8.11.2]:
∞
z a−1 X (−1)k (1 − a)k
Γ(a, z) ≈ z
e k=0
zk
(25)
No auxiliary data is needed for this algorithm, so it is thread-safe.
Riemann zeta function. For large positive arguments s (the present author
uses the condition s > 2.303d/ log(2.215d), where d is the precision in digits),
it suffices to use the definition of zeta, namely
ζ(s) =
∞
X
1
.
s
n
n=1
(26)
For modest-sized arguments, the zeta function can be evaluated by means of
this formula, due to Peter Borwein [11]. Select n to be the number of digits
of precision required for the result. Define
!
j−n
X
n!
− 2n ,
(27)
ej = (−1)j
k!(n
−
k)!
k=0
where the empty sum is zero. Then
2n−1
X
−1
ej
ζ(s) ≈ n
.
1−s
2 (1 − 2 ) j=0 (j + 1)s
(28)
The above formulas are used for positive real arguments (except s = 1, for
which the zeta function is undefined). For negative s, the following “reflection
formula” is used to convert the calculation to a positive argument:
ζ(s) =
2 cos(π(1 − s)/2)Γ(1 − s)ζ(1 − s)
.
(2π)1−s
25
(29)
Formulas (27), (28) and (29) are implemented as the zeta function. No
auxiliary data for this algorithm required, so it is thread-safe.
A even faster algorithm, based on the Euler-Maclaurin summation formula, can be derived from the following [16, 25.2.9]: Select an integer parameter N > 0 (the present author uses N = 0.6d, where d is the number of
digits of precision). Then
∞ N
X
X
1
1
s + 2k − 2
B2k
1
+
−
+
, (30)
ζ(s) ≈
s
s−1
s
s−1+2k
k
(s
−
1)N
2N
2kN
2k
−
1
k=1
k=1
where B2k are the even Bernoulli numbers
B2k =
(−1)k−1 2(2k)!ζ(2k)
.
(2π)2k
(31)
Since the zeta function evaluations in (31) are for positive even integer arguments, they can be calculated quickly using (28). Once the requisite even
Bernoulli numbers B2k (up to index k approximately matching the precision
level in digits) are computed, the function that implements formula (30) is
thread-safe.
5
Installation, compilation and linking
Installation, compilation and linking is relatively straightforward, provided
that you have a Unix-based system, such as Linux or Apple OSX, with a
command-line interface (such as the Terminal application of Apple OSX).
For Apple OSX systems, you first must install the “Command Line Tools”
package, which is available (for free) from the Apple Developer website. Instructions for doing this are provided in the README.txt file in the distribution package for either MPFUN-Fort or MPFUN-MPFR.
To install the MPFUN-MPFR version, you must first install the GMP and
MPFR packages. The latest versions are available from https://gmplib.org
and http://www.mpfr.org/mpfr-current/, respectively. Instructions for
installing GMP and MPFR are included in the README.txt file for MPFUNMPFR.
The gfortran compiler, which is highly recommended for either version of
MPFUN2015, is available (for free) for a variety of systems at the website
https://gcc.gnu.org/wiki/GFortranBinaries.
26
The MPFUN-Fort version also works with IBM’s xlf compiler, Intel’s
ifort and Portland Group’s pgf90. The MPFUN-MPFR version also works
with Intel’s ifort (it has not yet been tested on the other two). Compile-link
scripts for each supported compiler are provided in the distribution software
directory.
Each version of the software comes in two variants:
• Variant 1: This is recommended for basic applications that do not
dynamically change the precision level (or do so only rarely).
• Variant 2: This is recommended for more sophisticated applications
that dynamically change the precision level (see Section 6.4 below).
As an example, if one has installed the GNU gfortran compiler, then
variant 1 can be compiled by typing
./gnu-complib1.scr
Then the application program progx.f90 can be compiled and linked with
variant 1 of the library, producing the executable progx, by typing
./gnu-complink1.scr progx
These scripts assume that the user program is in the same directory as the
library files; this can easily be changed by editing the script files.
6
Fortran coding instructions
A high-level Fortran interface is provided for both versions of the package.
A C++ interface is planned but not complete.
As mentioned above, to use either version of the MPFUN2015 package,
the user first sets the parameter mpipl, the “default” precision level in digits,
which is the maximum precision level to be used for subsequent computation,
and is used to specify the amount of storage required for multiprecision data.
mpipl is set in a parameter statement at the start of module MPFUNF, which
is in file mpfunf.f90. In the code as distributed, mpipl is set to 1200 digits
(sufficient to run the six test programs of Section 7), but it can be set to any
level greater than or equal to 30 digits. mpipl is automatically converted to
mantissa words by the formula mpwds = int (mpipl / mpdpw + 2), where
mpdpw is a system parameter, and where int () means truncate to integer.
For MPFUN-Fort, mpdpw is log10 (248 ) = 14.44943979187 . . . , whereas for
MPFUN-MPFR it is log10 (264 ) = 19.26591972249 . . . (both values are double
27
precision approximations). The resulting parameter mpwds is the internal
default precision level, in words. All computations are performed to mpwds
words precision unless the user, in an application code, specifies a lower value.
After setting the value of mpipl in module MPFUNF, compile either
variant 1 or variant 2 of the library, using one of the scripts mentioned above.
Next, place the following line in every subprogram of the user’s application code that contains a multiprecision variable or array, at the beginning
of the declaration section, before any implicit or type statements:
use mpmodule
To designate a variable or array as multiprecision real (MPR) in an application program, use a Fortran-90 type statement with the type mp real, as in
this example:
type (mp real) a, b(m), c(m,n)
Similarly, to designate a variable or array as multiprecision complex (MPC),
use a type statement with the type mp complex. Thereafter when one of
these variables or arrays appears, as in the code
d = a + b(i) * sqrt(3.d0 - c(i,j))
the proper underlying multiprecision routines are automatically called.
Most common mixed-mode combinations (arithmetic operations, comparisons and assignments) involving MPR, MPC, double precision (DP), double
complex (DC), and integer operands are supported. A complete list of supported mixed-mode operations is given in Table 2. See Section 6.3 below
about DP and DC constants and expressions.
Input/output of MP variables or array elements is done using special
subroutines. See Table 6.1 and Section 6.2 for details.
In the MPFUN-Fort version, the above instructions apply if the precision
level, namely mpipl, is 19,500 digits or less. For higher precision, in addition
to changing mpipl to this higher level, one must call mpinit at the start
of execution, before any multiprecision computation is done. If this is a
multithreaded application, this initialization must be done in single-threaded
mode. With variant 1, subroutine mpinit has an optional argument, which is
the maximum precision level, in words; if not present, the default precision,
namely mpwds words (which corresponds to mpipl digits), is assumed. In
variant 2, this argument is required. See Section 6.4 for details. When the
initialization feature of MPFR is available, this same call will apply to the
MPFUN-MPFR version.
28
Operator
a = b
(assignment)
a**b
(ab )
<=, >=, <, >
(≤, ≥, <, > tests)
Arg 1
MPR
DP
Int
MPR
MPC
MPC
DP
DC
MPR
MPR
MPR
MPC
MPC
MPR
MPR
MPC
MPC
MPR
MPC
MPR
DP
MPR
Int
MPR
Arg 2
MPR
MPR
MPR
MPC
MPR
MPC
MPC
MPC
DP [1]
Int [1]
Char [1]
DP [1]
DC [1]
Int
MPR
Int
MPC
MPC
MPR
MPR
MPR
DP
MPR
Int
Operator
+, -, *, /
(+, −, ×, ÷)
Arg 1
MPR
DP
MPR
Int
MPR
MPC
DP
MPC
DC
MPC
MPR
MPC
Arg 2
MPR
MPR
DP
MPR
Int
MPC
MPC
DP
MPC
DC
MPC
MPR
==, /=
(=, 6= tests)
MPR
DP
MPR
Int
MPR
MPC
DP
MPC
DC
MPC
MPR
MPC
MPR
MPR
DP
MPR
Int
MPC
MPC
DP
MPC
DC
MPC
MPR
Table 2: Supported mixed-mode operator combinations. MPR denotes multiprecision real, MPC denotes multiprecision complex, DP denotes double
precision, DC denotes double complex, Int denotes integer and Char denotes
arbitrary-length character string. Note:
[1] These operations are not allowed in variant 2 — see Section 6.4.
29
Note in particular that for the time being, computations performed using
the MPFUN-MPFR version that involve transcendental functions are not
thread-safe, unless one has built the MPFR library with the thread-safe build
option. This limitation will be removed in a future release of the package.
6.1
Functions and subroutines
Most Fortran-2008 intrinsic functions [18] are supported in MPFUN2015 with
MPR and MPC arguments, as appropriate. A full listing of these functions
is shown in Table 3. In each case, these functions represent a straightforward
extension to MPR or MPC arguments, as indicated. Tables 4 and 5 present
a list of additional functions and subroutines provided in this package. In
these tables, “F” denotes function, “S” denotes subroutine, “MPR” denotes
multiprecision real, “MPC” denotes multiprecision complex, “DP” denotes
double precision, “DC” denotes double complex, “Int” denotes integer and
“Q” denotes IEEE quad precision (i.e., real*16), if supported. The variable
names r1,r2,r3 are MPR, z1 is MPC, d1 is DP, dc1 is DC, i1,i2,i3 are
integers, s1 is character*1, sn is character*n for any n, and rr is MPR of
length i1.
6.2
Input and output of multiprecision data
Binary-decimal conversion and input or output of multiprecision data is not
handled by standard Fortran read/write commands, but instead is handled
by special subroutines, as briefly mentioned in Table 4. Here are the details:
1. subroutine mpeform (r1,i1,i2,s1). This converts the MPR number r1 into character form in the character*1 array s1. The argument
i1 (input) is the length of the output string, and i2 (input) is the
number of digits after the decimal point. The format is analogous to
Fortran E format. The result is left-justified among the i1 cells of s1.
The condition i1 ≥ i2 +20 must hold.
2. subroutine mpfform (r1,i1,i2,s1). This converts the MPR number r1 into character form in the character*1 array s1. The argument
i1 (input) is the length of the output string, and i2 (input) is the
number of digits after the decimal point. The format is analogous to
Fortran F format. The result is right-justified among the i1 cells of s1.
The condition i1 ≥ i2 +10 must hold.
30
Type
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPC
MPR
MPC
MPR
DP
DC
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPR
MPC
MPR
MPR
MPR
Name
abs(r1)
abs(z1)
acos(r1)
acosh(r1)
aimag(r1)
aint(r1)
anint(r1)
asin(r1)
asinh(r1)
atan(r1)
atan2(r1,r2)
atanh(r1)
bessel j0(r1)
bessel j1(r1)
bessel jn(i1,r1)
bessel y0(r1)
bessel y1(r1)
bessel yn(i1,r1)
conjg(z1)
cos(r1)
cos(z1)
cosh(r1)
dble(r1)
dcmplx(z1)
erf(r1)
erfc(r1)
exp(r1)
exp(z1)
gamma(r1)
hypot(r1,r2)
log(r1)
log(z1)
log gamma(r1)
max(r1,r2)
min(r1,r2)
sign(r1,r2)
sin(r1)
sin(z1)
sinh(r1)
tan(r1)
tanh(r1)
Description
Absolute value.
Absolute value of complex arg.
Inverse cosine.
Inverse hyperbolic cosine.
Imaginary part of complex arg.
Truncate real to integer.
Round to closest integer.
Inverse sine.
Inverse hyperbolic sine.
Inverse tangent.
Arctangent with two args.
Inverse hyperbolic tangent.
Bessel function of the first kind, order 0.
Bessel function of the first kind, order 1.
Besel function of the first kind, order i1.
Bessel function of the second kind, order 0. [1]
Bessel function of the second kind, order 1. [1]
Besel function of the second kind, order i1. [1]
Complex conjugate.
Cosine of real arg.
Cosine of complex arg.
Hyperbolic cosine.
Convert MPR argument to DP.
Convert MPC argument to DC.
Error function. [1]
Complementary error function. [1]
Exponential function of real arg.
Exponential function of complex arg.
Gamma function.
Hypotenuse of two args.
Natural logarithm of real arg.
Natural logarithm of complex arg.
Log gamma function [1].
Maximum of two (or three) args.
Minimum of two (or three) args.
Transfer of sign from r2 to r1.
Sine function of real arg.
Sine function of complex arg.
Hyperbolic sine.
Tangent function.
Hyperbolic tangent function.
Table 3: Fortran-2008 intrinsic functions extended to multiprecision. Notes:
[1]: Currently available only with MPFUN-MPFR.
31
Type
F(MPC)
F(MPC)
F(MPC)
F(MPC)
S
Name
mpcmplx(r1,r2)
mpcmplx(dc1)
mpcmplx(z1)
mpcmplxdc(dc1)
mpcssh(r1,r2,r3)
S
mpcssn(r1,r2,r3)
S
mpeform(r1,i1,i2,s1)
S
mpfform(r1,i1,i2,s1)
F(MPR)
S
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
S
mpegamma()
mpinit
mplog2()
mpnrt(r1,i1)
mppi()
mpprodd(r1,d1)
mpquotd(r1, d1)
mpread(i1,r1)
S
mpread(i1,z1)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(Int)
F(Int)
S
mpreal(r1)
mpreal(z1)
mpreal(d1)
mpreal(q1)
mpreal(s1,i1)
mpreal(sn)
mpreald(d1)
mpwprec(r1)
mpwprec(z1)
mpwrite(i1,i2,i3,r1)
S
mpwrite(i1,i2,i3,z1)
F(Q)
qreal(r1)
Description
Converts (r1,r2) to MPC. [1]
Converts DC arg to MPC. [1]
Converts MPC arg to MPC. [1]
Converts DC to MPC, without checking. [1, 2]
Returns both cosh and sinh of r1, in the same
time as calling just cosh or just sinh.
Returns both cos and sin of r1, in the same
time as calling just cos or just sin.
Converts r1 to char*1 string in Ei1.i2
format, suitable for output (Sec. 6.2).
Converts r1 to char*1 string in Fi1.i2
format, suitable for output (Sec. 6.2).
Returns Euler’s γ constant. [1, 3]
Initializes for extra-high precision (Sec. 6). [1]
Returns log(2). [1]
Returns the i1-th root of r1.
Returns π. [1]
Returns r1*d1, without checking. [2]
Returns r1/d1, without checking. [2]
Inputs r1 from Fortran unit i1; up to five
MPR args may be listed (Sec. 6.2). [1]
Inputs z1 from Fortran unit i1; up to five
MPC args may be listed (Sec. 6.2). [1]
Converts MPR arg to MPR. [1]
Converts MPC arg to MPR. [1]
Converts DP arg to MPR. [1, 2]
Converts real*16 to MPR. [1, 2]
Converts char*1 string to MPR (Sec. 6.2). [1]
Converts char*n string to MPR (Sec. 6.2). [1]
Converts DP to MPR, without checking. [1, 2]
Returns precision in words assigned to r1.
Returns precision in words assigned to z1.
Outputs r1 in Ei2.i3 format to unit i1; up to
five MPR args may be listed (Sec. 6.2).
Outputs z1 in Ei2.i3 format to unit i1; up to
five MPC args may be listed (Sec. 6.2) .
Converts MPR to real*16.
Table 4: Additional general routines (F: function, S: subroutine). Notes:
[1]: In variant 1, an integer precision level argument (mantissa words) may
optionally be added as the final argument; this argument is required in variant 2. See Section 6.4.
[2]: These do not check DP or DC values. See Section 6.3.
[3]: These are currently only available with MPFUN-MPFR.
32
Type
F(MPR)
F(MPR)
S
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
F(MPR)
Name
agm(r1,r2)
airy r1)
berne(i1,rr)
besselj(r1,r2)
digamma(r1)
expint(r1)
gammainc(r1,r2)
polylog(n1,r1)
zeta(r1)
zetaem(n1,rr,r1)
Description
Arithmetic-geometric mean.
Airy function. [3]
Array of first i1 even Bernoulli numbers. [1, 2]
BesselJ function with two MPR arguments. [2]
Digamma function. [3]
Exponential integral function. [3]
Incomplete gamma function. [2]
Polylogarithm function [3, 4].
Zeta function.
Zeta function with precomputed even
Bernoulli numbers. [2]
Table 5: Additional special functions. Notes:
[1]: In variant 1, an integer precision level argument (mantissa words) may
optionally be added as the final argument; this argument is required in variant 2. See Section 6.4.
[2]: These functions are currently only available with MPFUN-Fort.
[3]: These functions are currently only available with MPFUN-MPFR.
[4]: Currently restricted to n1 = 2.
3. subroutine mpread (i1,r1). This reads the MPR number r1 from
Fortran logical unit i1. The digits of r1 may span more than one
line, provided that a backslash (\) appears at the end of a line to be
continued. Individual input lines may not exceed 2048 characters in
length. Format: The input number must have a leading digit (possibly
zero), and must have a period somewhere, but must not have embedded
blanks; an exponent with e, d, E or D may optionally follow the numeric value. Up to five MPR arguments may be included in argument
list. See item 9 below on an additional precision argument.
4. subroutine mpread (i1,z1). This is the same as the previous item
(3), except that the input argument z1 is of type MPC (a pair of MPR).
Up to five MPC arguments may be included in argument list. See item
9 below on an additional precision argument.
5. function mpreal (s1,i1). This converts the string s1, which is of
type character*1 and length i1, to MPR. See item 3 for format. See
item 9 below on an additional precision argument.
33
6. function mpreal (sn). This converts the string sn, which may be of
type character*n for any n, to MPR. See item 3 for format. On some
systems, n may be limited, say to 2048; if this is a problem, use previous
item (5). See item 9 below on an additional precision argument.
7. subroutine mpwrite (i1,i2,i3,r1). This writes the MPR number
r1 to Fortran logical unit i1. The argument i2 (input) is the length
of the output field, and i3 (input) is the number of digits after the
decimal point. The format is analogous to Fortran E format and is
left-justified in the field. Up to five MPR arguments may be included
in argument list.
8. subroutine mpwrite (i1,i2,i3,z1). This is the same as the previous item (7), except that the argument z1 is of type MPC (a pair of
MPR). Up to five MPC arguments may be included in argument list.
9. Note: For mpread (items 3 and 4) and mpreal (items 5 and 6), when
using variant 1, an integer precision level argument (mantissa words)
may optionally be added as the final argument; this argument is required in variant 2. See Section 6.4.
6.3
Handling double precision values
Double precision constants and expressions are indispensable in high-precision
applications. For one thing, the product, say, of a multiprecision value times
a double precision value is more rapidly computed with a routine dedicated
to this task than converting the double precision value to multiprecision and
then calling the multi-multi multiplication routine. Certainly the usage of
double precision constants such as modest-sized whole numbers and exact
binary fractions (e.g., 0., 12345., 3.125), which are entirely safe in a multiprecision application, should be allowed.
However, problems can arise, which are inherent in how modern computer
languages handle double precision constants and expressions. For example,
the code
r1 = 3.14159d0
where the left-hand side is multiprecision, does not produce the full-precision
equivalent of 3.14159, since by standard rules of precedence, the right-hand
side is evaluated in double precision, then converted (by zero extension) to
34
the left-hand side. When using the package, one can avoid this problem by
writing this as
r1 = ’3.14159’
By enclosing the constant in apostrophes (and changing it to a literal), this
indicates to the MPFUN2015 software that the constant is to be evaluated
to full precision.
A closely related problem is the following: The code
r2 = r1 + 3.d0 * sqrt (2.d0)
does not produce a fully accurate result, since the subexpression 3.d0 *
sqrt (2.d0) is performed in double precision (again, according to standard
rules of operator precedence in all major programming languages). The solution here is to write this as
r2 = r1 + 3.d0 * sqrt (mpreal (2.d0))
or, if using variant 2, as
r2 = r1 + 3.d0 * sqrt (mpreal (2.d0, nwds))
where nwds is the precision level, in words, to be assigned to the constant 2
(see Section 6.4). This forces all operations to be done using MP routines.
To help avoid such problems, the MPFUN2015 low-level software checks
every double precision value (constants, variables and expression values) in
a multiprecision statement at execution time to see if it has more than 40
significant bits. If so, it is flagged as an error, since very likely such usage
represents an unintended loss of precision in the application program. This
feature catches 99.99% of accuracy loss problems due to the usage of inexact
double precision values.
On the other hand, some applications contain legitimate double precision constants that are trapped by this test. For example, in the tpslqm2
and tpslqm3 programs listed in Section 7, exact double precision values can
arise that are greater than 40 bits in size. In order to permit such usage,
four special functions have been provided: mpprodd, mpquotd, mpreald,
mpcmplxdc (see Table 4). The first and second return the product and quotient, respectively, of a MPR argument and a DP argument; the third converts a DP value to MPR (with an optional precision level parameter — see
Section 6.4); and the fourth converts a DC value to MPC (with an optional
precision level parameter — see Section 6.4). These routines do not check
the double precision argument to see if it has more than 40 significant bits.
35
6.4
Dynamically changing the working precision
Different applications have different requirements for language support. One
distinction that immediately arises is between applications that do not need
to change the working precision from the initially-defined default level (or
change it only rarely) and those which, usually for performance reasons,
require that the working precision be changed frequently.
Accordingly, for both MPFUN-Fort and MPFUN-MPFR, there are two
variants of the language interface module MPFUNG (see Section 3.2):
1. Variant 1: This is recommended for basic applications that do not
dynamically change the precision level (or do so only rarely).
2. Variant 2: This is recommended for more sophisticated applications
that dynamically change the precision level.
In particular, with variant 1:
1. Assignments of the form R = X, where R is MPR and X is DP, integer
or literal are permitted. Assignments of the form Z = Y, where Z is
MPC and Y is DP or DC, are permitted.
2. The routines mpcmplx, mpcmplxdc, mpegamma, mpinit, mplog2,
mppi, mpread, mpreal and mpreald each have an (optional) integer
argument as the final argument in the list. This argument is the precision level, in words, to be assigned to the result(s). If this argument
is not present, the default precision level (mpwds words, corresponding
to mpipl digits) is assumed.
In contrast, with variant 2:
1. The assignments mentioned in item 1 above are not permitted. If any
of these appears in code, compile-time errors will result. Instead, one
must use mpreal and mpcmplx, as appropriate, with the precision level
(in mantissa words) as the final argument, to perform these conversions.
2. The optional precision level arguments mentioned in item 2 above are
required in all cases.
Note that the mpreal function, with the precision level (in words) as the
second argument, can be used to assign an MPR argument with one precision
36
level to an MPR variable or array element with a different working precision
level. The same is true of mpcmplx. The working precision currently assigned
to any MP variable or array element may be obtained by using the function
mpwprec — see Table 4.
Along this line, when one uses the precision level arguments, a precision
level of ndig digits can be converted to words by the formula nwds = int
(ndig / mpdpw + 2). By using the global built-in variable mpdpw (which is
different between MPFUN-Fort and MPFUN-MPFR) in this way, the user
code remains portable between the two versions.
As it turns out, in most applications, even those that frequently require
the working precision to be changed, only a few changes need to be made to
the source code. Consider, for example, the following user code, where the
default precision is set in module MPFUNF as 1200 digits:
integer k, nx
parameter (nx = 128)
type (mp real) x(nx)
...
x(1) = 1.d0
do k = 2, nx
x(k) = 2.d0 * x(k-1) + 1.d0
enddo
This code, as written, is permissible with variant 1, but not with variant
2, because the assignment x(k) = 1.d0 is not allowed. Furthermore, all
operations are performed with the default (maximum) precision level of 1200
digits. So with variant 2, where one wishes to perform this loop with a
precision level of approximately 200 digits, this should be written as:
integer k, ndig, nwds, nx
parameter (nx = 128, ndig = 200, nwds = int (ndig / mpdpw + 2))
type (mp real) x(nx)
...
x(1) = mpreal (1.d0, nwds)
do k = 2, nx
x(k) = 2.d0 * x(k-1) + 1.d0
enddo
37
Note that by changing x(1) = 1.d0 to x(1) = mpreal (1.d0, nwds),
the array element x(1) is assigned the value 1.0, with a working precision of
nwds words (i.e., 200 digits). In the loop, when k is 2, x(2) also inherits the
working precision level nwds words, since it is computed with an expression
that involves x(1). By induction, all elements of the array x inherit the
working precision level nwds words (i.e., 200 digits).
This scenario is entirely typical of other types of algorithms and applications — in most cases, it is only necessary to make a few code changes, such
as in assignments to double precision values before a loop, to completely control dynamic precision. It is recommended, though, that the user employ the
system function mpwprec, which returns the working precision level currently
assigned to an input multiprecision variable or array element (see Table 4),
to ensure that the precision level the user thinks is assigned to a variable is
indeed the level being used by the program.
Using variant 2, with its stricter coding standards, requires a bit more
programming effort, but in the present author’s experience, when dealing
with applications that dynamically change the precision level, this additional
effort is more than repaid by fewer debugging and performance problems in
actual usage. A code written for variant 2 also works with variant 1, but not
vice versa. See the sample test codes mentioned in the next section, all of
which are written to conform to the standards of variant 2.
7
Performance of sample applications
Numerous full-scale multiprecision applications have been implemented using
the MPFUN2015 software, including some that dynamically vary the working
precision level. In most cases, only very minor modifications needed to be
made to existing double precision source code. Some examples include the
following:
1. tpslq1: A one-level standard PSLQ program; finds the coefficients of
the degree-30 polynomial satisfied by 31/5 − 21/6 . Size of code: 752
lines. Precision level: 240 digits.
2. tpslqm1: A one-level multipair PSLQ program; finds the coefficients
of the degree-30 polynomial satisfied by 31/5 − 21/6 . Size of code: 905
lines. Precision level: 240 digits.
38
3. tpslqm2: A two-level multipair PSLQ program; finds the coefficients
of the degree-56 polynomial satisfied by 31/7 − 21/8 . Size of code: 1694
lines. Precision level: 640 digits; switches frequently between multiprecision and double precision.
4. tpslqm3: A three-level multipair PSLQ program; finds the coefficients
of the degree-72 polynomial satisfied by 31/8 − 21/9 . Size of code: 2076
lines. Precision level: 1100 digits; switches frequently between full
precision, medium precision (120 digits) and double precision.
5. tquad: A quadrature program; performs the tanh-sinh, the exp-sinh
or the sinh-sinh quadrature algorithm, as appropriate, on a suite of 18
problems involving numerous transcendental function references, producing results correct to 500-digit accuracy. Size of code: 1421 lines.
Precision level: 1000 digits, but most computation is done to 500 digits;
switches frequently between 500 and 1000 digits.
6. tpphix3: A Poisson phi program; computes the value of φ2 (x, y) and
then employs a three-level multipair PSLQ algorithm to find the minimal polynomial of degree m satisfied by exp(8πφ2 (1/k, 1/k)) for a given
k (see Section 1.4). In the code as distributed, k = 30, m = 64, and
a palindromic option is employed so that the multipair PSLQ routines
(which are part of this application) searches for a relation of size 33
instead of 65. This computation involves transcendental functions and
both real and complex multiprecision arithmetic. Size of code: 2506
lines. Precision level: 1100 digits; switches frequently between full precision, medium precision (160 digits) and double precision.
Each of the above six programs, with a reference output file for comparison, is included in the software’s distribution package. These codes work
identically with both the MPFUN-Fort and the MPFUN-MPFR versions —
no changes are required to switch from one version to the other. The script
mpfun-tests.scr, which is included in the distribution package for each version, compiles variant 2 of the library, then compiles, links and runs all six
of the test programs, plus a brief validity check program.
If, after running the test script, the results reproduce the results in the
reference output files (except possibly the CPU run times and the iteration
counts in the PSLQ runs), then one can be fairly confident that the software is
working properly. Note that the final output relation of the PSLQ runs might
39
have all signs reversed from the reference output (which is mathematically
equivalent).
These programs are provided, in part, as examples of programming techniques when using the MPFUN2015 package. Users may feel free to adapt
these codes, although the present author would appreciate being notified and
credited when this is done. All application programs and library codes are
publicly available but are subject to copyright and other legal conditions.
For details, see the file disclaimer.txt in the distribution package.
7.1
Timings
Table 6 presents some performance timings comparing the two versions of the
package, and for the GNU gfortran and Intel ifort compilers. Note that with
the MPFUN-MPFR version, the GNU C compiler was used to build the GMP
and MPFR libraries, no matter what compiler was employed to compile the
MPFUN-MPFR libraries and the application code. These runs were made on
a 2.9 GHz MacBook Pro with an Intel Core i5 processor and 8 Gbyte of main
memory. For uniformity, the timings are listed in the Table 6 to two decimal
place accuracy, but, as with all computer run time measurements, they should
not be considered repeatable beyond about two or three significant digits.
The tested codes include the first six applications listed in the previous
section, and, in addition, the Poisson phi program, which is performed for a
range of problem sizes without the palindromic option.
Examining these results, and focusing on the longer-running programs in
the suite, we observe a roughly 3X performance advantage for the MPFUNMPFR version over the MPFUN-Fort version, and a roughly 25% performance advantage for the Intel ifort compiler over the GNU gfortran compiler. Note that for the programs tquadts and tquadtsp, which perform numerical integration using tanh-sinh quadrature, MPFUN-MPFR is roughly
nine times faster, with either compiler, than MPFUN-Fort. This reflects the
very fast performance of transcendental functions in MPFR. For the other
applications, which are dominated by algebraic operations, the ratio is lower.
8
Conclusion
The current version of the software and documentation is available at:
http://www.davidhbailey.com/dhbsoftware
40
Code name
tpslq1
tpslqm1
tpslqm2
tpslqm3
tquad
tpphix3
tpphix3
tpphix3
tpphix3
tpphix3
tpphix3
Precision
(digits)
240
240
650
1100
1000
700
2000
2100
2700
5000
8200
tpphix3
param.
k
m
18
22
17
26
25
32
36
60
64
72
100
128
GNU gfortran
MPFUN- MPFUNFort
MPFR
16.45
4.33
13.91
2.94
15.96
7.97
69.52
32.47
76.07
7.59
7.34
2.63
93.39
35.93
144.91
52.05
261.50
94.55
2023.77
663.69
9704.11
3239.61
Intel ifort
MPFUN- MPFUNFort
MPFR
11.22
4.03
4.63
2.61
11.94
5.78
39.76
26.96
76.83
7.48
3.72
2.18
65.82
30.75
89.94
43.91
185.31
80.18
1423.03
501.51
7318.59
2221.86
Table 6: Timings on a suite of test programs (seconds).
41
References
[1] D. H. Bailey and J. M. Borwein, “High-precision arithmetic in
mathematical physics,” Mathematics, vol. 3 (2015), pg. 337–367.
http://www.mdpi.com/2227-7390/3/2/337/pdf.
[2] D. H. Bailey, X. S. Li and K. Jeyabalan, “A comparison of three
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14 (2005), no. 3, pg. 317–329.
[3] D. H. Bailey, R. Barrio, and J. M. Borwein, “High precision
computation: Mathematical physics and dynamics,” Applied
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[4] D. H. Bailey and J. M. Borwein, “Hand-to-hand combat with
thousand-digit integrals,” Journal of Computational Science, vol. 3
(2012), pg. 77-86.
[5] D. H. Bailey, J. M. Borwein, R. E. Crandall and J. Zucker, “Lattice
sums arising from the Poisson equation,” Journal of Physics A:
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[6] D. H. Bailey and J. M. Borwein, “Compressed lattice sums arising
from the Poisson equation: Dedicated to Professor Hari Sirvastava,”
Boundary Value Problems, vol. 75 (2013), DOI:
10.1186/1687-2770-2013-75,
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[7] D. H. Bailey, J. M. Borwein and R. E. Crandall, “Integrals of the Ising
class,” Journal of Physics A: Mathematical and General, vol. 39
(2006), pg. 12271–12302.
[8] D. H. Bailey and D. J. Broadhurst, “Parallel integer relation detection:
Techniques and applications,” Mathematics of Computation, vol. 70,
no. 236 (Oct 2000), pg. 1719–1736.
[9] D. H. Bailey, X. S. Li and B. Thompson, “ARPREC: An arbitrary
precision computation package,” Sep 2002,
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[10] J. M. Borwein and D. H. Bailey, Mathematics by Experiment: Plausible
Reasoning in the 21st Century, 2nd ed., A. K. Peters, Natick, MA,
2008.
[11] P. Borwein, “An efficient algorithm for the Riemann zeta function,”
1995, http://www.cecm.sfu.ca/~pborwein/PAPERS/P155.pdf.
[12] R. P. Brent, “Fast multiple-precision evaluation of elementary
functions,” J. of the ACM, vol. 23 (1976), 242–251.
[13] R. P. Brent and P. Zimmermann, Modern Computer Arithmetic,
Cambridge Univ. Press, 2010.
[14] Jingwei Chen, Damien Stehle and Gilles Villard, “A new view on
HJLS and PSLQ: Sums and projections of lattices,” Proc. of
ISSAC’13, 149–156.
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[16] “Digital library of mathematical functions,” National Institute of
Standards and Technology, 2015, http://dlmf.nist.gov.
[17] H. R. P. Ferguson, D. H. Bailey and S. Arno, “Analysis of PSLQ, an
integer relation finding algorithm,” Mathematics of Computation, vol.
68, no. 225 (Jan 1999), pg. 351–369.
[18] “Fortran 2008,”
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[20] “GNU MPFR library: Comparison of multiple-precision floating-point
software,” http://www.mpfr.org/mpfr-current/timings.html.
[21] Y. Hida, X. S. Li and D. H. Bailey, “Algorithms for Quad-Double
Precision Floating Point Arithmetic,” Proc. of the 15th IEEE
Symposium on Computer Arithmetic (ARITH-15), 2001.
[22] A. K. Lenstra, H. W. Lenstra, and L. Lovasz, “Factoring polynomials
with rational coefficients,” Mathematische Annalen, vol. 261 (1982),
pg. 515-534.
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[23] “MPFR C++,” http://www.holoborodko.com/pavel/mpfr.
[24] NIST Digital Library of Mathematical Functions, version 1.0.6 (May
2013), http://dlmf.nist.gov.
[25] R. W. Potter, Arbitrary Precision Calculation of Selected Higher
Functions, Lulu.com, San Bernardino, CA, 2014.
[26] C. Rubio-Gonzalez, C. Nguyen, H. D. Nguyen, J. Demmel, W. Kahan,
K. Sen, D. H. Bailey, C. Iancu and D. Hough, “Precimonious: Tuning
assistant for floating-point precision,” Proceedings of SC13, 26 Apr
2013,
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[27] H. Takahasi and M. Mori, “Double exponential formulas for numerical
integration,” Publications of RIMS, Kyoto University, vol. 9 (1974),
pg. 721-741.
[28] S. W. Williams and D. H. Bailey, “Parallel computer architecture,” in
David H. Bailey, Robert F. Lucas and Samuel W. Williams, ed.,
Performance Tuning of Scientific Applications, CRC Press, Boca
Raton, FL, 2011, 11–33.
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