A Comparative Study of SYCL, OpenCL, and OpenMP

Silva, Hercules Cardoso Da; Pisani, Flávia; Borin, Edson

doi:10.1109/sbac-padw.2016.19

Cited by 27 publications

(9 citation statements)

References 4 publications

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“…Silva et al compare the performance of two kernels written in SYCL, OpenCL, and OpenMP on CPU devices [11]; GPUs were not considered in their study.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the performance of HPC-style SYCL applications

Deakin

McIntosh–Smith

2020

Proceedings of the International Workshop on OpenCL

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SYCL is a parallel programming model for developing single-source programs for running on heterogeneous platforms. To this end, it allows for one code to be written which can run on a different architectures. For this study, we develop applications in SYCL which are representative of those often used in High-Performance Computing. Their performance is benchmarked on a variety of CPU and GPU architectures from multiple vendors, and compared to well optimised versions written in OpenCL and other parallel programming models. CCS CONCEPTS• General and reference → Performance; • Computing methodologies → Parallel programming languages; Massively parallel and high-performance simulations; • Software and its engineering → Parallel programming languages.

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“…Silva et al compare the performance of two kernels written in SYCL, OpenCL, and OpenMP on CPU devices [11]; GPUs were not considered in their study.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the performance of HPC-style SYCL applications

Deakin

McIntosh–Smith

2020

Proceedings of the International Workshop on OpenCL

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“…It uses standard C++, and its programming model allows providing kernel and host code in one file ("single-source programming"). In [80], the authors compare overall performance (number of API calls, memory usage, processing time) and easy of use of SYCL with OpenMP and OpenCL. Two benchmarks are provided: Common Midpoint (CMP) used in seismic processing and 27stencil which is one of the OpenACC benchmarks and is similar to algorithms for solving partial differential equations.…”

Section: Comparisons Of Existing Parallel Programming Approaches For mentioning

confidence: 99%

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

Czarnul

Proficz

Drypczewski

2020

Scientific Programming

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This paper provides a review of contemporary methodologies and APIs for parallel programming, with representative technologies selected in terms of target system type (shared memory, distributed, and hybrid), communication patterns (one-sided and two-sided), and programming abstraction level. We analyze representatives in terms of many aspects including programming model, languages, supported platforms, license, optimization goals, ease of programming, debugging, deployment, portability, level of parallelism, constructs enabling parallelism and synchronization, features introduced in recent versions indicating trends, support for hybridity in parallel execution, and disadvantages. Such detailed analysis has led us to the identification of trends in high-performance computing and of the challenges to be addressed in the near future. It can help to shape future versions of programming standards, select technologies best matching programmers’ needs, and avoid potential difficulties while using high-performance computing systems.

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“…There are comparative studies to compare the efficiency of parallel-computation platforms [18][19][20][21]. According to [18], SYCL takes longer time to execute common midpoint operation, compared to OpenCL and OpenMP. However, in the point of memory usages, SYCL used the least memory amount among another platforms.…”

Section: Cuda (Compute Unified Device Architecture) Opencl (Open Commentioning

confidence: 99%

“…There are studies about the performance comparison between platforms, as shown in [18], or experiments using triSYCL, as shown in [27]. However, there are very few studies about performance measures on each SYCL implementation.…”

Section: Comparisons Of Sycl Implementationsmentioning

confidence: 99%

Large-Scale Data Computing Performance Comparisons on SYCL Heterogeneous Parallel Processing Layer Implementations

2020

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Today, many big data applications require massively parallel tasks to compute complicated mathematical operations. To perform parallel tasks, platforms like CUDA (Compute Unified Device Architecture) and OpenCL (Open Computing Language) are widely used and developed to enhance the throughput of massively parallel tasks. There is also a need for high-level abstractions and platform-independence over those massively parallel computing platforms. Recently, Khronos group announced SYCL (C++ Single-source Heterogeneous Programming for OpenCL), a new cross-platform abstraction layer, to provide an efficient way for single-source heterogeneous computing, with C++-template-level abstractions. However, since there has been no official implementation of SYCL, we currently have several different implementations from various vendors. In this paper, we analyse the characteristics of those SYCL implementations. We also show performance measures of those SYCL implementations, especially for well-known massively parallel tasks. We show that each implementation has its own strength in computing different types of mathematical operations, along with different sizes of data. Our analysis is available for fundamental measurements of the abstract-level cost-effective use of massively parallel computations, especially for big-data applications.

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A Comparative Study of SYCL, OpenCL, and OpenMP

Cited by 27 publications

References 4 publications

Evaluating the performance of HPC-style SYCL applications

Evaluating the performance of HPC-style SYCL applications

Survey of Methodologies, Approaches, and Challenges in Parallel Programming Using High-Performance Computing Systems

Large-Scale Data Computing Performance Comparisons on SYCL Heterogeneous Parallel Processing Layer Implementations

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