Black-Scholes Option Pricing on Intel CPUs and GPUs: Implementation on SYCL and Optimization Techniques

TL;DR

This paper demonstrates how to develop and optimize high-performance, portable Black-Scholes option pricing code using SYCL on Intel CPUs and GPUs, achieving near-C++ performance on CPUs and reasonable GPU performance.

Contribution

It presents a systematic approach to port and optimize a financial benchmark from C++ to SYCL, highlighting techniques for CPU and GPU performance enhancement.

Findings

SYCL code is only 10% slower than optimized C++ on CPUs

Achieves reasonable performance on Intel GPUs

Provides optimization guidelines for device-friendly memory access and data management

Abstract

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.