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Proceedings of the 8th Workshop on High Performance Computational Finance最新文献

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GPU option pricing GPU期权定价
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-11-15 DOI: 10.1145/2830556.2830564
Simon Suo, Ruiming Zhu, Ryan Attridge, J. Wan
{"title":"GPU option pricing","authors":"Simon Suo, Ruiming Zhu, Ryan Attridge, J. Wan","doi":"10.1145/2830556.2830564","DOIUrl":"https://doi.org/10.1145/2830556.2830564","url":null,"abstract":"In this paper, we explore the possible approaches to harness extra computing power from commodity hardware to speedup pricing calculation of individual options. Specifically, we leverage two parallel computing platforms: Open Computing Language (OpenCL) and Compute United Device Architecture (CUDA). We propose several parallel implementations of the two most popular numerical methods of option pricing: Lattice model and Monte Carlo method. In the end, we show that the parallel implementations achieve significant performance improvement over serial implementations.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133644338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Parallelism-centric optimization and performance study of a finance aggregation engine on modern NUMA systems 现代NUMA系统上财务聚合引擎的并行优化与性能研究
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-11-15 DOI: 10.1145/2830556.2830558
Guojing Cong, Sophia Wen, James Sedgwick, Louis Ly
{"title":"Parallelism-centric optimization and performance study of a finance aggregation engine on modern NUMA systems","authors":"Guojing Cong, Sophia Wen, James Sedgwick, Louis Ly","doi":"10.1145/2830556.2830558","DOIUrl":"https://doi.org/10.1145/2830556.2830558","url":null,"abstract":"Mark-to-future aggregation is a key component of counterparty credit risk analysis in the IBM Algorithmics software. Its computation exhibits complex memory access and control flow patterns, and is hard to accelerate. The prior effort to improve performance takes a \"pre-compiled\" approach that aims to reduce the overhead and inefficiencies primarily through compiler techniques. While combined with other optimizations, the performance is improved by 3 to 5 times, many extra lines of code are dynamically generated. Maintenance and testing become a challenge. In our study we take a parallelism centric approach guided by hardware counter based profiling. Minimal modifications are made to the code and about 10 times speedup is achieved. We also study the behavior of mark-to-future aggregation on a NUMA platform. We evaluate the impact of architectural choices on the performance. Our study sheds some light on accelerating mark-to-future aggregation on current and emerging architectures.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129012168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimization strategies for portable code for Monte Carlo-based value-at-risk systems 基于蒙特卡罗的风险价值系统的可移植代码的优化策略
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-11-15 DOI: 10.1145/2830556.2830559
J. Varela, Claus Kestel, C. D. Schryver, N. Wehn, Sascha Desmettre, R. Korn
{"title":"Optimization strategies for portable code for Monte Carlo-based value-at-risk systems","authors":"J. Varela, Claus Kestel, C. D. Schryver, N. Wehn, Sascha Desmettre, R. Korn","doi":"10.1145/2830556.2830559","DOIUrl":"https://doi.org/10.1145/2830556.2830559","url":null,"abstract":"Value-at-risk (VaR) computations are one important basic element of risk analysis and management applications. On the one hand, risk management systems need to be flexible and maintainable, but on the other hand they require a very high computational power. In general, accelerators provide high speedups, but come with a limited flexibility. In this work, we investigate two approaches towards portable and fast code for VaR computations on heterogeneous platforms: operator tuning and the use of OpenCL. We show that operator tuning can save up one third of run time on CPU-based systems in the calibration step. For OpenCL, we present a detailed analysis of run time on CPU, GPU, and Xeon Phi, and evaluate its portability. We also find that the same code runs up to 12x faster in a VaR setting with an accelerator card being present, without any code changes required.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125687301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Potential future exposure, modelling and accelerating on GPU and FPGA 潜在的未来曝光，建模和加速GPU和FPGA
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-11-15 DOI: 10.1145/2830556.2830560
Grzegorz Kozikowski, Grigorios Papamanousakis, Jinzhe Yang
{"title":"Potential future exposure, modelling and accelerating on GPU and FPGA","authors":"Grzegorz Kozikowski, Grigorios Papamanousakis, Jinzhe Yang","doi":"10.1145/2830556.2830560","DOIUrl":"https://doi.org/10.1145/2830556.2830560","url":null,"abstract":"Counterparty Credit Risk is of top concern among financial institutions, as the over-the-counter derivative market has been growing rapidly for the last two decades. Potential Future Exposure (PFE) provides assessment of the safety of a bank's asset portfolio and its adequacy by evaluating whether it is resilient under severely stressing market movements. This paper proposes a PFE model that fits specific business requirements, as well as a GPU and a FPGA implementation of such model. The FPGA implementation has been optimised in terms of the performance to support a fully pipelined design. Experimental results show that the GPU implementation can achieve up to 25 times speedup over CPU solution, and the FPGA implementation can achieve up to 120 times speedup.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117343298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Fulfilling solvency II regulations using high performance computing 使用高性能计算实现偿付能力II法规
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-11-15 DOI: 10.1145/2830556.2830561
Mark Tucker, J. M. Bull
{"title":"Fulfilling solvency II regulations using high performance computing","authors":"Mark Tucker, J. M. Bull","doi":"10.1145/2830556.2830561","DOIUrl":"https://doi.org/10.1145/2830556.2830561","url":null,"abstract":"Throughout Europe, Solvency II Regulations are changing the way in which companies involved in the provision of financial services must assess their solvency. Historically, solvency has been assessed using a single 'best estimate' set of assumptions. Solvency II requires that a Monte Carlo approach is used to find a 1-in-200 worst-case scenario; this demands computing power which is outside the realms of anything currently available in the industry. In this paper, we demonstrate that the new regulations can be met by moving away from the currently-used actuarial valuation software packages, and running well-performing ab initio code in an HPC environment. Our implementation uses a combination of algorithmic improvements, serial optimisations and large scale parallelism which allows a complete assessment calculation on a representative portfolio of annuities in well under one hour. This work brings the Monte Carlo simulations within the bounds of practicality.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117275840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
STAC-A2™ benchmark on POWER8 POWER8上的STAC-A2™基准测试
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-11-15 DOI: 10.1145/2830556.2830557
B. Brock, Frank Liu, K. Rajamani
{"title":"STAC-A2™ benchmark on POWER8","authors":"B. Brock, Frank Liu, K. Rajamani","doi":"10.1145/2830556.2830557","DOIUrl":"https://doi.org/10.1145/2830556.2830557","url":null,"abstract":"The STAC-A2™ benchmark is an emerging standard designed to evaluate the speed, scalability and quality of computational platforms for performing financial risk analytics in the capital markets industry. The problem posed by the benchmark is the computation of several types of Greeks for an exotic option under an American exercise model. We recently reported record-setting performance for a STAC-A2 benchmark solution developed for an IBM® POWER8® S824 server. We explain the high performance of our solution in terms of the architecture, scalability and high memory bandwidth provided by POWER8 based systems. Developing the benchmark application also led us to investigate and perfect several techniques that are generally applicable to the simulation of complex options and their sensitivities. We describe several of these techniques in detail, along with the performance impacts we observed when compared with other approaches. We focus on two areas in particular, namely cache-efficient data management for Monte Carlo simulation of American-exercise options, and a parallel implementation of the Longstaff-Schwartz algorithm.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132535418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Implementing deep neural networks for financial market prediction on the Intel Xeon Phi 在Intel Xeon Phi处理器上实现金融市场预测的深度神经网络
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2015-07-06 DOI: 10.1145/2830556.2830562
M. Dixon, D. Klabjan, J. Bang
{"title":"Implementing deep neural networks for financial market prediction on the Intel Xeon Phi","authors":"M. Dixon, D. Klabjan, J. Bang","doi":"10.1145/2830556.2830562","DOIUrl":"https://doi.org/10.1145/2830556.2830562","url":null,"abstract":"Deep neural networks (DNNs) are powerful types of artificial neural networks (ANNs) that use several hidden layers. They have recently gained considerable attention in the speech transcription and image recognition community (Krizhevsky et al., 2012) for their superior predictive properties including robustness to overfitting. However their application to financial market prediction has not been previously researched, partly because of their computational complexity. This paper describes the application of DNNs to predicting financial market movement directions. A critical step in the viability of the approach in practice is the ability to effectively deploy the algorithm on general purpose high performance computing infrastructure. Using an Intel Xeon Phi co-processor with 61 cores, we describe the process for efficient implementation of the batched stochastic gradient descent algorithm and demonstrate a 11.4x speedup on the Intel Xeon Phi over a serial implementation on the Intel Xeon.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"128 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131462351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 51
Proceedings of the 8th Workshop on High Performance Computational Finance 第八届高性能计算金融研讨会论文集
Proceedings of the 8th Workshop on High Performance Computational Finance Pub Date : 2009-11-15 DOI: 10.1145/2830556
D. Daly, M. Eleftheriou, J. Moreira, K. D. Ryu
{"title":"Proceedings of the 8th Workshop on High Performance Computational Finance","authors":"D. Daly, M. Eleftheriou, J. Moreira, K. D. Ryu","doi":"10.1145/2830556","DOIUrl":"https://doi.org/10.1145/2830556","url":null,"abstract":"Computational finance is an important multi-disciplinary research field. The traditional approaches are falling short, as the volume of data and complexity of the models increase at an astonishing rate. 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 continues to grow in volume and complexity, and algorithmic trading becomes increasingly popular, there is increased demand for computational power. This is transforming the way that financial markets do business and it offers potential major innovations. Yet, the field is still in its infancy. Only in recent years have we started to see growth in relevant research and in the number of workshops in the area of high performance computational finance. \u0000 \u0000When we organized the First Workshop on High Performance Computational Finance in 2008, we had envisioned bringing together practitioners, researchers, vendors, and scholars from the complementary fields of computational finance and high performance computing. Our goal was to promote an exchange of ideas and research, discuss future research collaborations and develop new research directions. The success of that first workshop motivated us to organize this second edition. Recent events in the world economy have demonstrated a great need for better models and tools to perform risk analysis and risk management. Therefore, we selected risk management as a focus area for the 2009 workshop. \u0000 \u0000We have assembled a diverse program, consisting of three keynotes, one invited talk and five presentations selected from the set of submitted papers. Our speakers come from the financial industry, from computer vendors and from academia. Topics range from management of value at risk to frameworks for market data processing to exploitation of new computer architectures. We are confident that this workshop will help stimulate the research in the area of high performance computational finance.","PeriodicalId":254831,"journal":{"name":"Proceedings of the 8th Workshop on High Performance Computational Finance","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116987382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
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