Workshop on high‐performance computational finance

Abstract

EDITORIALWorkshop on high-performance computational finance BACKGROUNDThe purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the Seventh Workshop on High-Performance Computational Finance, held in conjunction with SC'14. This annual workshop brings together practitioners, researchers, vendors, and scholars from the complementary fields of computational finance and high-performance computing in order to promote exchange of ideas, discuss future collaborations and develop new research directions. Financial companies increasingly rely on high performance computers to analyze high volumes of financial data, automatically execute trades, and manage risk.As financial market data continue to grow in volume and complexity, computational capabilities of emerging hardware also increases. Extracting high performance from emerging architectures requires a combination of domain knowledge and specialized technical skills. This special issues presents many examples of how researchers, scholars, vendors, and practitioners are collaborating to address high-performance computing research challenges.The scope of this special issue is broad and is representative of the multi-disciplinary nature of computational finance. In addition to submissions that deal with performance and programmability challenges, theoretical analysis, algorithms, and practical experience in computational finance, this issue also includes articles that address practical challenges with running financial applications in the cloud.The efficient deployment of applications on multi and many-core architectures is a central topic in This issue would be incomplete without coverage of the highly impactful STAC A2 benchmark, which is designed to measure the performance of market risk analysis workloads. A. Nikolaev, B. Ilya, and S. Sania [4] describe how Intel is actively contributing to the specification and provides updated benchmark implementations optimized for the Intel Xeon processor E5 and E7 product families and Intel Xeon Phi coprocessor.Performance versus accuracy of computational methods is a pervasive topic in the financial modeling. C. Brugger, G. Liu, C. De Schryver, and N. Wehn [5] consider the calibration of various models with closed form solutions and propose a hybrid solution.We encourage the reader to review [6][7][8] to gain insight into the breadth and depth of problems and innovative solutions in the multi-disciplinary field of high-performance computational finance.