OpenMP device offloading to FPGA accelerators

Sommer, Lukas; Korinth, Jens; Koch, Andreas

doi:10.1109/asap.2017.7995280

Cited by 45 publications

(21 citation statements)

References 11 publications

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“…ExaHyPE, an Exascale Hyperbolic PDE design [30] used a pragma-based GPU parallelization approach for object-oriented code, and documented lessons learned. Several other related works include demonstrating GPU support for OpenMP offloading features in compilers in Flang/Clang [3,25], a proof-ofconcept implementation of offloading for FPGA based accelerators [14,26], and an interprocedural statical analysis heuristic at runtime to select optimal grid sizes for offloaded target team constructs [27], among others. There are publicly available benchmark suites to evaluate heterogeneous application performance, e.g.…”

Section: Related Workmentioning

confidence: 99%

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Davis

Daley

Pophale

et al. 2021

Accelerator Programming Using Directives

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Heterogeneous systems are becoming increasingly prevalent. In order to exploit the rich compute resources of such systems, robust programming models are needed for application developers to seamlessly migrate legacy code from today's systems to tomorrow's. Over the past decade and more, directives have been established as one of the promising paths to tackle programmatic challenges on emerging systems. This work focuses on applying and demonstrating OpenMP offloading directives on five proxy applications. We observe that the performance varies widely from one compiler to the other; a crucial aspect of our work is reporting best practices to application developers who use OpenMP offloading compilers. While some issues can be worked around by the developer, there are other issues that must be reported to the compiler vendors. By restructuring OpenMP offloading directives, we gain an 18x speedup for the su3 proxy application on NERSC's Cori system when using the Clang compiler, and a 15.7x speedup by switching max reductions to add reductions in the laplace mini-app when using the Cray-llvm compiler on Cori.

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Section: Related Workmentioning

confidence: 99%

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Davis

Daley

Pophale

et al. 2021

Accelerator Programming Using Directives

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show abstract

“…In addition, these techniques can reduce the number of required HLS pragmas which further simplifies FPGA programming in HLS. Another recent change is the support of OpenMP [16,117,134]. OpenMP is one of the most popular languages for parallel programming for shared memory architecture.…”

Section: Programmability Trendsmentioning

confidence: 99%

“…Thus, a future hardware compiler might take the shared memory feature into account. A first step to start exploring this direction is to support OpenMP for hardware compilation [117,134]. OpenMP has been a popular parallel computing programming language for shared memory.…”

Section: Compilation For Fpgasmentioning

confidence: 99%

In-memory database acceleration on FPGAs: a survey

et al. 2019

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While FPGAs have seen prior use in database systems, in recent years interest in using FPGA to accelerate databases has declined in both industry and academia for the following three reasons. First, specifically for in-memory databases, FPGAs integrated with conventional I/O provide insufficient bandwidth, limiting performance. Second, GPUs, which can also provide high throughput, and are easier to program, have emerged as a strong accelerator alternative. Third, programming FPGAs required developers to have full-stack skills, from high-level algorithm design to low-level circuit implementations. The good news is that these challenges are being addressed. New interface technologies connect FPGAs into the system at mainmemory bandwidth and the latest FPGAs provide local memory competitive in capacity and bandwidth with GPUs. Ease of programming is improving through support of shared coherent virtual memory between the host and the accelerator, support for higher-level languages, and domain-specific tools to generate FPGA designs automatically. Therefore, this paper surveys using FPGAs to accelerate in-memory database systems targeting designs that can operate at the speed of main memory.

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“…Most of the rest of the literature that strictly follows the OpenMP accelerator model studied GPU offloading and demonstrate good results [23,24]. Nevertheless, some of them look for more untraditional targets like the Intel Xeon Phi platform or FPGAs [65]. Several interesting works also explore the usability of the programming model as well as the performance portability of the application over different platforms [66][67][68].…”

Section: Related Workmentioning

confidence: 99%

Cluster Programming using the OpenMP Accelerator Model

Yviquel

Cruz

Araújo

2018

ACM Trans. Archit. Code Optim.

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Computation offloading is a programming model in which program fragments (e.g., hot loops) are annotated so that their execution is performed in dedicated hardware or accelerator devices. Although offloading has been extensively used to move computation to GPUs, through directive-based annotation standards like OpenMP, offloading computation to very large computer clusters can become a complex and cumbersome task. It typically requires mixing programming models (e.g., OpenMP and MPI) and languages (e.g., C/C++ and Scala), dealing with various access control mechanisms from different cloud providers (e.g., AWS and Azure), and integrating all this into a single application. This article introduces computer cluster nodes as simple OpenMP offloading devices that can be used either from a local computer or from the cluster head-node. It proposes a methodology that transforms OpenMP directives to Spark runtime calls with fully integrated communication management, in a way that a cluster appears to the programmer as yet another accelerator device. Experiments using LLVM 3.8, OpenMP 4.5 on well known cloud infrastructures (Microsoft Azure and Amazon EC2) show the viability of the proposed approach, enable a thorough analysis of its performance, and make a comparison with an MPI implementation. The results show that although data transfers can impose overheads, cloud offloading from a local machine can still achieve promising speedups for larger granularity: up to 115× in 256 cores for the 2MM benchmark using 1GB sparse matrices. In addition, the parallel implementation of a complex and relevant scientific application reveals a 80× speedup on a 320 core machine when executed directly from the headnode of the cluster. CCS Concepts: • Computing methodologies → Distributed programming languages; • Software and its engineering → Parallel programming languages; Distributed programming languages; Runtime environments; Source code generation;

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OpenMP device offloading to FPGA accelerators

Cited by 45 publications

References 11 publications

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

Performance Assessment of OpenMP Compilers Targeting NVIDIA V100 GPUs

In-memory database acceleration on FPGAs: a survey

Cluster Programming using the OpenMP Accelerator Model

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