Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on SYCL and Optimization Techniques
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866917718219816960 |
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| author | Panova, Elena Volokitin, Valentin Gorshkov, Anton Meyerov, Iosif |
| author_facet | Panova, Elena Volokitin, Valentin Gorshkov, Anton Meyerov, Iosif |
| contents | The Black-Scholes option pricing problem is one of the widely used financial benchmarks. We explore the possibility of developing a high-performance portable code using the SYCL (Data Parallel C++) programming language. We start from a C++ code parallelized with OpenMP and show optimization techniques that are beneficial on modern Intel Xeon CPUs. Then, we port the code to SYCL and consider important optimization aspects on CPUs and GPUs (device-friendly memory access patterns, relevant data management, employing vector data types). We show that the developed SYCL code is only 10% inferior to the optimized C++ code when running on CPUs while achieving reasonable performance on Intel GPUs. We hope that our experience of developing and optimizing the code on SYCL can be useful to other researchers who plan to port their high-performance C++ codes to SYCL to get all the benefits of single-source programming. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_13740 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on SYCL and Optimization Techniques Panova, Elena Volokitin, Valentin Gorshkov, Anton Meyerov, Iosif Distributed, Parallel, and Cluster Computing Mathematical Software Performance The Black-Scholes option pricing problem is one of the widely used financial benchmarks. We explore the possibility of developing a high-performance portable code using the SYCL (Data Parallel C++) programming language. We start from a C++ code parallelized with OpenMP and show optimization techniques that are beneficial on modern Intel Xeon CPUs. Then, we port the code to SYCL and consider important optimization aspects on CPUs and GPUs (device-friendly memory access patterns, relevant data management, employing vector data types). We show that the developed SYCL code is only 10% inferior to the optimized C++ code when running on CPUs while achieving reasonable performance on Intel GPUs. We hope that our experience of developing and optimizing the code on SYCL can be useful to other researchers who plan to port their high-performance C++ codes to SYCL to get all the benefits of single-source programming. |
| title | Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on SYCL and Optimization Techniques |
| topic | Distributed, Parallel, and Cluster Computing Mathematical Software Performance |
| url | https://arxiv.org/abs/2204.13740 |