Toward a Unified GPU-Aware OpenSHMEM Specification

πŸ“… 2026-07-08
πŸ“ˆ Citations: 0
✨ Influential: 0
πŸ“„ PDF
πŸ€– AI Summary
This work addresses the lack of portable memory semantics for GPU architectures in existing OpenSHMEM implementations, which has led to fragmented vendor-specific approaches. To resolve this, the authors propose a lightweight, backward-compatible auxiliary specification that, for the first time, defines a vendor-neutral GPU-scoped memory space abstraction along with a complementary capability query mechanism. This unified framework harmonizes the semantics of remote memory access (RMA), atomic operations, synchronization, and collective operations under both host-initiated and device-initiated execution models. Built upon the Partitioned Global Address Space (PGAS) paradigm, the proposal is accompanied by a prototype implementation deployable across multiple GPU vendors, thereby establishing the first practical foundation and standardization pathway for GPU extensions to the OpenSHMEM specification.
πŸ“ Abstract
Leadership-class HPC systems are now accelerator-centric, with GPUs providing most floating-point throughput and memory bandwidth. As next-generation systems increasingly integrate accelerators through high-speed memory fabrics and system interconnects, exposing larger tightly coupled device domains, \ac{PGAS} models such as OpenSHMEM provide a natural abstraction for expressing fine-grained remote memory operations across these devices. While OpenSHMEM 1.x offers a lean PGAS model for irregular communication, atomics, fine-grained synchronization, and collectives, its memory model lacks portable semantics for accelerator architectures. As a result, existing GPU-enabled OpenSHMEM implementations differ in memory management, capability discovery, and operation semantics, limiting portability and ecosystem cohesion. This risks fracturing the community that OpenSHMEM was originally created to unify. This paper proposes an OpenSHMEM Auxiliary Specification for GPU-Aware Communication, designed as a lightweight, backward-compatible extension to OpenSHMEM 1.x. The auxiliary specification introduces a minimal memory model extension via a GPU-scoped memory space abstraction, along with capability queries and well-defined semantics for using \acs{GPU}-attached buffers in RMA, atomic, synchronization, and collective operations. This is initially conceived through the lens of a host-initiated interface, although it provides a general set of semantics that also allow for optional device-initiated support. A central goal of this effort is to demonstrate that GPU-aware OpenSHMEM semantics can be specified and implemented across GPUs from multiple vendors, providing a practical and rapidly implementable step toward unification under a vendor-neutral specification while informing the design of future OpenSHMEM specifications.
Problem

Research questions and friction points this paper is trying to address.

OpenSHMEM
GPU-aware
PGAS
memory model
portability
Innovation

Methods, ideas, or system contributions that make the work stand out.

GPU-aware
OpenSHMEM
PGAS
memory model
portability
πŸ”Ž Similar Papers
No similar papers found.
N
Naveen Ravi
Hewlett Packard Enterprise (HPE), Bloomington, Minnesota, USA
N
Nathan Wichmann
Hewlett Packard Enterprise (HPE), Bloomington, Minnesota, USA
M
Md. Wasi-ur- Rahman
Hewlett Packard Enterprise (HPE), Bloomington, Minnesota, USA
Aurelien Bouteiller
Aurelien Bouteiller
University of Tennessee, Knoxville
Computer ScienceHigh Performance ComputingFault ToleranceMPI
Y
YΔ±ltan Hassan TemuΓ§in
Advanced Micro Devices, Inc., Austin, Texas, USA
A
Avinash Kethineedi
Advanced Micro Devices, Inc., Austin, Texas, USA
Johnathan Alsop
Johnathan Alsop
AMD Research
B
Brandon Potter
Advanced Micro Devices, Inc., Austin, Texas, USA
S
Shubhendra Pal Singhal
Georgia Institute of Technology, Atlanta, Georgia, USA
Jun Shirako
Jun Shirako
Georgia Institute of Technology
Parallel programming languagesoptimizing compilerssynchronization runtimes
Akihiro Hayashi
Akihiro Hayashi
Georgia Institute of Technology
Parallel LanguagesProgramming ModelsCompilersRuntime SystemsQuantum Computing
Vivek Sarkar
Vivek Sarkar
School of Computer Science, Georgia Institute of Technology
Parallel computingProgramming languagesCompilersProgram AnalysisRuntime Systems
Lawrence C. Stewart
Lawrence C. Stewart
Serissa Research
High performance computingCommunicationsComputer architecture
M
Michael Beebe
Texas Tech University, Lubbock, Texas, USA
B
Benjamin Michalowicz
The Ohio State University, Columbus, Ohio, USA
Jeongnim Kim
Jeongnim Kim
Intel
Condensed matter physicsmaterialsquantum chemistrycomputational physics
Thiago Teixeira
Thiago Teixeira
Google
M
Mark F. Brown
Intel Corporation, Hillsboro, Oregon, USA
A
Aaron Welch
Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA
Oscar Hernandez
Oscar Hernandez
Oak Ridge National Laboratory
High Performance ComputingCompilersCode Transformation ToolsPerformance Optimizations
W
Wendy Poole
Los Alamos National Laboratory, Los Alamos, New Mexico, USA
S
Steve Poole
Los Alamos National Laboratory, Los Alamos, New Mexico, USA