Abstract
Partial wave analysis(PWA) is an important tool in hadron physics. Large data sets from the experiments in high precision frontier require high computational power. To utilize GPU cluster and the resource of super computers with various types of accelerator, we implement a software framework for partial wave analysis using OpenAcc, OpenAccPWA. OpenAccPWA provides convenient approaches for exposing parallelism in the code and excellent support for the large amount of existing CPU-based codes of partial wave amplitudes. It can avoid heavy workload of code migration from CPU to GPU. This proceeding will briefly introduce the software framework and performance of OpenAccPWA.
Highlights
GPUPWA at BESIIIThe Beijing Spectrometer III (BES-III) is an important particle physics experiment at the Beijing Electron–Positron Collider II (BEPC-II) at the Institute of High Energy Physics(IHEP)
Introduction to OpenAccThe OpenACC Application Program Interface describes a collection of compiler directives to specify loops and regions of code in standard C, C++ and Fortran to be offloaded from a host CPU to an attached accelerator device, providing portability across operating systems, host CPUs and accelerators.At its core OpenACC supports offloading of both computation and data from a host device to an accelerator device
To utilize GPU cluster and the resource of super computers with various types of accelerator, we implement a software framework for partial wave analysis using OpenAcc, OpenAccPWA based on GPUPWA
Summary
The Beijing Spectrometer III (BES-III) is an important particle physics experiment at the Beijing Electron–Positron Collider II (BEPC-II) at the Institute of High Energy Physics(IHEP). The pioneer approach of harnessing GPU parallel acceleration in PWA was performed in the framework of BES-III.[4]. BES-III developed GPUPWA software framework based on OpenCL. GPUPWA uses the programming language of C + +, and its functions of fitting and drawing are realized by ROOT.[5]. IHEP has established a GPU High Performance Computing Cluster. On an Intel Core 2 Quad 2.4 GHz workstation with 2 GB RAM and an ATI Radeon 4870 GPU with 512 MB RAM, a J⁄ψ → γK+K− analysis with four partial waves runs more than 100 times faster than the reference FORTRAN implementation for sufficiently large numbers of events
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