论文标题
黑色chleos选项定价在Intel CPU和GPU上：SYCL和优化技术实现
Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on SYCL and Optimization Techniques
论文作者
论文摘要
Black-Scholes选项定价问题是广泛使用的财务基准之一。我们探索使用SYCL（数据并行C ++）编程语言开发高性能便携式代码的可能性。我们从与OpenMP并行的C ++代码开始，并显示对现代Intel Xeon CPU有益的优化技术。然后，我们将代码移植到SYCL，并考虑CPU和GPU（设备友好的内存访问模式，相关数据管理，采用向量数据类型）的重要优化方面。我们表明，在CPU上运行时，开发的SYCL代码仅比优化的C ++代码低10％，同时在Intel GPU上实现合理的性能。我们希望我们在SYCL上制定和优化代码的经验对其他计划将其高性能C ++代码移植到SYCL的研究人员很有用，以获得单源节目的所有好处。
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.
