Instruction Set Extensions for Photonic Synchronous

Instruction Set Extensions for Photonic Synchronous
Coalesced Accesses
Paul Keltcher, David Whelihan, Jeffrey Hughes
Massachusetts Institute of Technology Lincoln Laboratory
Lexington, MA.
Abstract— Microprocessors have evolved over the last fortyplus years from purely sequential single operation machines, to
pipelined super-scalar, to threaded and SIMD, and finally to
multi-core and massive multi-core/thread machines. Despite
these advances, the conceptual model programmers use to
program them is still that of a single threaded register file bound
math unit that can only be loosely synchronized with other such
processors. This lack of explicit synchrony, caused by limitations
of metal interconnect, limits parallel efficiency. Recent advances
in silicon photonic-enabled architectures [1, 5, 7] promise to
greatly enable high synchrony over long distances (centimeters or
more). In this paper, it is shown that global synchrony changes
the way computers can be programmed by introducing a new
class of ISA level instruction: the globally-synchronous load-store.
In the context of multiple load-store machines, the globally
synchronous load-store architecture allows the programmer to
think about a collection of independent load-store machines as a
single load-store machine. This operation is described, and its
ISA implications explored in the context of the distributed matrix
transpose, which exhibits a high degree of data non-locality, and
is difficult to efficiently parallelize on modern architectures.
Keywords—computer architectures; shared memory; photonics;
instruction set; coalesced memory
An Instruction Set Architecture (ISA) is the representation
of an underlying computer architecture used by a programmer
to realize application goals. The ISA exports the sum total of
all capabilities of a computer to the programmer and embodies
the way it is intended to be used in a collection of instructions
accessible to a programmer.
Despite the addition of
performance-improving features such as super-scalar and
caching, the programmer’s mental model of a processor has
changed little in 40 years. Therefore, the way computers are
programmed has not changed significantly either.
Unfortunately, the stalling of frequency scaling in the early part
of the last decade, and the resultant shift from faster gates to
more parallel gates has not resulted proportionally to increased
performance in part because the sequential programming style
useful in Instruction-Level Parallelism (ILP) exploiting superscalar processors is at best ineffective in an explicitly parallel
context, and at worst detrimental.
This work is sponsored by Defense Advanced Research Projects Agency
(DARPA) under Air Force contract FA8721-05-C-0002. Opinions,
interpretations, conclusions and recommendations are those of the author and
are not necessarily endorsed by the United States Government. Distribution
Statement A. Approved for public release; distribution is unlimited.
978-1-4799-1365-7/13/$31.00 ©2013 IEEE
As parallel shared memory computers have become larger,
incorporating tens of independent super-scalar cores, latency
in interconnection networks has become the dominant factor
limiting performance. Computer architects have attempted to
mitigate the effects of latency by [8]:
• Adding instructions to prefetch data or have more
explicit control over the behavior of the memory subsystem.
• Increasing the number and proximity of caches to the
execution core.
• Increasing threading, thereby hiding the effects of
None of these remedies are particularly efficient from an
energy perspective. All are the result of acceptance that
electrical interconnect latency is an intractable problem. The
ultimate effect imposed by this limitation is that processing
hardware cannot be synchronized over long distances. This
makes highly efficient parallel programming of many-core
architectures difficult, and highly dependent upon
architectural parameters.
When a fundamental limitation of underlying technology
is reached, new technology must be introduced to move
forward. In [5] the addition of silicon photonics to the
architect’s technological toolbox is shown to enable scalable
parallel efficiency in dense processing loads with very low
data locality. In that paper, the Synchronous Coalesced
Access (SCA), which alleviates the effects of non-locality by
reorganizing data in-flight in a photonic waveguide, is
introduced. This capability is shown to greatly increase
parallel efficiency by removing uncertainty within the
interconnect and memory subsystem, permitting processors to
operate in lock-step, with high efficiency.
This high degree of global synchrony permits a new
paradigm in parallel programming: the globally synchronous
load/store. The contribution of this paper is to present
computer instructions that express global load-store behavior
in a massively multi-core system in which individual
processors do not need to understand the entire global
load/store data access patter n. This capability and its role in
the normal program flow is described in the context of a GPUstyle architecture.
Inbound (SCA-1) Waveguide
Outbound (SCA) Waveguide
Fig. 1: P-Sync Architecture
Fig. 2: Matrix Mapping
Section II briefly introduces the photonically-enabled Psync architecture [5], and the SCA operation. Section III
describes how multi-dimensional matrices are mapped to
linear memory. In Section IV, new ISA extensions are
defined, and their effects on the code stream for the
challenging distributed matrix transpose operation are
illustrated. Related work, specifically how existing parallel
architectures work with distributed data, is discussed in
section V, followed by conclusions in section VI.
matrix, the row ordered memory mapping is extremely
Explicit whole structure data reorganizations such as
transpose are commonly used to pre-stage data in memory in
order to increase access efficiency over a set of operations
defined by the application. Because PSCAN can synchronize
all processors, it is possible for those processors to use a
global map that defines exactly when each processor will
write to the memory with the goal of synthesizing optimally
structured memory accesses. Figure 2 shows an abstract
matrix mapped to 1-D memory. The matrix is stored in rowmajor order, with each row sequentially stored contiguously in
increasing memory locations. Because an entire memory line
must be read whenever any element on that line is needed,
accessing this matrix in row-order is maximally efficient. If,
however, the columns must be operated on, only a part of each
memory line is needed for any column. To get any two
elements any matrix column together, two separate line reads
would occur with only half of the retrieved data used.
In [5] a photonic network called PSCAN is described that
utilizes chip-scale photonic networks for globally synchronous
communication. Instead of using chip-scale photonics as a
means of increased bandwidth density, photonics is utilized
for global synchrony over long distances. This permits
spatially separate processors to arbitrarily reorder data by
synthesizing monolithic transactions in the photonic
waveguide. Memory read/write operations are performed
without any special buffering resulting in optimal use of
channel and memory bandwidth and near 100% efficient use
of computation.
The P-Sync architecture uses PSCANs, shown in Figure 1,
where processors P0 to PN-1 are “worker” processors and the
“Head” node coordinates memory traffic. There is one
PSCAN network to the shared memory for reads and one for
writes. Like the GPU machine model, each worker processor
is relatively simple and has a large instruction issue width.
The differences with the GPU model is that P-Sync can
coalesce an arbitrary amount of data from all processors to the
global memory, whereas GPU coalescing across processors is
potentially inefficient, unscheduled, and limited to small
blocks of memory. The global DRAM is visible to all
Data reorganization occurs because multi-dimensional data
structures must be processed along different abstract
dimensions. Since computer memory is a linear resource,
addresses increase sequentially, and there is an optimal way to
access memory based on its internal structure. If a matrix is
mapped to linear memory by sequencing each row in order,
sequential operations on matrix elements are efficient. If,
however, a processor needs to operate on columns of the
In this paper, a map defines a pattern with which multiple,
spatially separate processors will write data to memory to
realize an abstract structure. A map always specifies a
contiguous access. If, in an MxN matrix, the number of rows
M is equal to the number of processors P operating on the
matrix, and each processor can hold all N elements of each
row, a map for transpose is defined by these parameters:
base – The base address of the mapped write
P – The number of processors (or hardware threads)
participating in the synthesized access
S – The number of bytes written by each processor
during its portion of the map
B – The number of blocks of size s
The pseudo code that describes this mapping of local noncontiguous data held in processors to a global address space is
shown here:
for i in [0 : B – 1]:
for j in [ 0 : P – 1]:
for k in [0 : S – 1]:
processor[j].write (
local_data[i*S + k],
base + i*j + k)
To illustrate the use of new instructions in the context of
matrix transpose, we adopt NVIDIA’s PTX as an ISA
foundation [6]. PTX is the intermediate assembly language
used by NVIDIA processors. A PTX program is a thread in
the data parallel SIMT (single instruction multiple thread)
programming model and is agnostic of actual machine
resources. Rather, the “thread” knows where it is relative to
the data (vector, matrix, or 3D space). The run-time
environment will parallelize the PTX application according to
available resources. PTX allows the expression of parallelism
without knowledge of available parallel resources. For this
analysis the basic architecture is taken from Figure 1, where
there is a coordinating processor (‘head node’) and worker
processors. Two instructions are added, a “global” instruction
that runs on the head node and a “local” instruction that runs
on worker processors.
the data, the worker processors then execute the following
base_address, S, B
// set up the coalescing write for all
// worker processors to participate in.
// Parameters are base address pointer
// (transposed row destination), B (number
// of blocks of size S) and S. The
// base_address is computed from the row
// pointer, rc
add.32 rc, rc, 1
sub.32 r, r, 1
// decrement loop count (r) and increment
// row pointer (rc)
bra loop
// continue until all rows have been
// coalesced.
The code represents an SCA operation used to write a single
matrix column back to memory, with N operations required to
write the entire matrix back to memory in column major form.
The coalesce_sca instruction is a blocking instruction that will
not complete until all data is received from the worker
processors. Whereas the coalesce_sca sets up a large
contiguous block of memory, each worker processor writes to
a different n byte space within that larger block. To transfer
// loop count
// the size of a row
// loop index
// compute local memory address based on
// thread id, blocking (if blocked), r2,
// and put in r5
ld.local.u32 r4, local[r5];
// read element out of local memory
r4, r3;
// participate in the global coalescing
// store. Each worker thread writes 4
// bytes of data held in r4 into the
// global SCA space at position r3. This
// is a blocking instruction and will not
// proceed until this threads “time” comes
// up on the photonic TDM waveguide.
In this example, each thread will hold one row of the NxM
matrix such that the number of hardware threads P=M. This
analysis also assumes there is enough local memory to hold
one row. In these new instructions, the head node initiates a
coalescing read or write and the worker processors execute
memory transactions relative to the control processor’s
initiation. To start each processor holds one row of the matrix
in local memory and will write out the transpose. The
following are the instructions for the head node processor:
mov.u32 r, N
mov.u32 rc, 0
// set up a loop to go over the entire
// matrix, one row at a time.
r1, N
r2, [row]
r3, 0
add.u32 r2, r2, 1;
sub.u32 r1, r1, 1;
// decrement loop count (r1) and
// increment row pointer (r2)
bra loop;
// continue until all 32bit elements have
// been coalesced.
This assumes the multiple threads on one processor can
coalesce their row reads from local memory, much the same
as the GPU architecture coalesces row reads from multiple
threads within a warp to the GPU global memory [10]. Unlike
loads and stores in a CPU or GPU, the SCA instructions allow
the hardware to order writes to memory across independent
Individually, the processors are writing
transposed data with no spatial locality, but globally the
memory write is perfectly contiguous within the sca_coalesce
space. This globally synchronous load store architecture
changes the way programmers can think about shared memory
programming. What was, in the general purpose processor, a
collection of independent load-store transactions, is instead
presented as one single memory transaction in which
individual processors order themselves relative to a global
What separates this work from existing parallel
architectures is the notion of combining memory traffic of
multiple independent processors into a single efficient
memory transaction. In this section we look at general
purpose CPUs, GPUs, Cray XMT and Oracle Macro-Chip to
understand how these architectures interface multiple
independent load-store streams.
General purpose CPUs, in trying to be general purpose, take
architectural innovations from other more special purpose
processors. To deal with high latency memories modern
general purpose machines manage latency by ISA additions
such a prefetch, use caches and hardware prefetchers to reduce
latency, amortize memory latency with vector instructions
when possible and utilize limited hardware threading. While
all of these innovations generally provide benefit to many
single threaded applications, these constructs when applied to
multi-core/multi-threaded applications with poor coordination
between processors in a shared memory system can result in a
performance penalty. The SCA instructions presented in the
previous section, however, allow each processor to explicitly
order themselves within a single larger transaction. This type
of optimization is not possible with existing general purpose
GPUs contain a large number of processing units which
operate similar to SIMD units found in CPUs, are more
special-purpose and excel when the application exhibits data
parallelism. Prior to NVIDIA’s Kepler processor[6] data
sharing between threads within a warp required a store and
load operation. Kepler added a Shuffle instruction that allows
arbitrary permutations within a warp. Although this special
instruction was added for communication between threads
within a warp, GPUs have no synchronous, efficient global
communication between warps. The programmer is required
to explicitly move data between GPU devices via the CPU’s
memory, and like general purpose CPUs, multiple GPU
processors cannot combine independent memory transactions
from multiple processors into a single contiguous memory
transaction. Still, if the data fits within the GPU (no off-chip
interconnection requirements) and the application has
sufficient parallelism (to amortize the on-chip interconnection
limitations), performance can be quite high.
The Cray XMT [3] architecture does not fight the reality of
high latencies within the interconnection network, but rather is
architected to tolerate latency for parallel applications which
exhibit a low degree of data locality. The XMT tolerates
memory latency of large shared memory systems by using
hundreds of threads per processor. While most threads are
waiting for data, there should be at least one thread which can
make forward progress. While this serves the machine well
for sparse data like graph traversal [9] and sparse linear
algebra [3], the XMT cannot optimize performance or
efficiency for applications that have dense memory access
patterns. Each thread in the XMT acts alone, so coordinating
load-store instructions between threads into larger transactions
for efficient memory usage is not possible.
The Sun/Oracle Macrochip [7] employs a point-to-point
photonic interconnection between compute nodes on a single
The goal of the macro-chip is to obtain
performance like a traditional CMP, but with many more
processors enabled by the high-bandwidth low-latency pointto-point interconnection between the cores on the virtual
“chip”. The compute nodes of the macro-chip are traditional
general purpose processors, although any type of compute
node could be imagined. To date there have been no proposed
ISA additions to take advantage of photonics.
This paper presents two new instructions, a global coalesce
instruction and a local SCA instruction that allows
programmers to express globally synchronous load-store
communication across multiple processors. This globally
synchronous load-store architecture permits programmers to
take a collection of independent load-store processors and
combine their transactions into a single monolithic memory
transaction. This presents new programming possibilities for
optimizing memory and network traffic, which are not
possible with the existing single-threaded view load-store
Going forward, there are more instructions to
investigate to further exploit the shared globally synchronous
P-Sync architecture.
N. Bliss, K. Asonovic, K. Bergman, L. Carloni, J. Kepner, and V.
Stojanovic, “Photonic Many-Core Architecture Study,” in Proceedings
of the Twelfth Annual Workshop on High Performance Embedded
Computing (HPEC) 2008.
[2] Shekhar Borkar, Pradeep Dubey, Kevin Kahn, David Kuck, Hans
Mulder, Stephen Pawlowski, Justin Rattner, “Platform 2015: Intel
Processor and Platform Evolution for the Next Decade”,
2015.pdf, Page 6.
[3] John Feo, David Harper, Simon Kahan, Petr Konecny, “Eldorado,” in
Proceedings of the 2nd conference on Computing Frontiers, ACM, May
[5] David Whelihan, Michelle Beard, Jeffrey Hughes, Anna Klein, Sanjeev
Mohindra, Julie Mullen, Eric Robinson, Scott Sawyer, Michael Wolf,
Nadya Bliss, Jonnie Chan, Robert Hendry, Keren Bergman, Luca
Carloni, “P-sync: A Photonically Enabled Architecture for Efficient
Non-local Data Access,” in IEEE International Parallel and Distributed
Processing Symposium (IPDPS), 2013
[7] Pranay Koka, Michael McCracken, Herb Schwetman, Xuezhe Zheng,
Ron Ho, Ashok Krishnamoorthy, “Silicon-photonic Network
Architectures for Scalable, Power-efficient Multi-chip Systems,” in
Proceedings of the 37th Annual international Symposium on Computer
Architecture, 2010.
[8] David Mizell, Cray Inc., “Introduction to the Cray XMT”,, 2010
[9] Georage Chin, Andres Marquez, Sutanay Choudhury, “Implementing
and Evaluating Multithreaded Triad Census Algorithms on the Cray
XMT,” in IEEE International Symposium on Parallel and Distributed
Processings, 2009
[10] Greg Ruetsch, Paulius Micikevicius, “Optimizing Matrix Transpose in
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