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OneGraph:a cross-architecture framework for large-scale graph computing on GPUs based on oneAPI 认领 引用 被引量：1
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作者 Shiyang Li Jingyu Zhu +6 位作者 Jiaxun Han Yuting Peng Zhuoran Wang Xiaoli Gong Gang Wang Jin Zhang Xuqiang Wang 《CCF Transactions on High Performance Computing》 EI 2024年第2期179-191,共13页
The explosive growth of graph data sets has led to an increase in the computing power and storage resources required for graph computing.To handle large-scale graph processing,heterogeneous platforms have become neces... The explosive growth of graph data sets has led to an increase in the computing power and storage resources required for graph computing.To handle large-scale graph processing,heterogeneous platforms have become necessary to provide suf-ficient computing power and storage.The most popular scheme for this is the CPU-GPU architecture.However,the steep learning curve and complex concurrency control for heterogeneous platforms pose a challenge for developers.Additionally,GPUs from different vendors have varying software stacks,making cross-platform porting and verification challenging.Recently,Intel proposed a unified programming model to manage multiple heterogeneous devices at the same time,named oneAPI.It provides a more friendly programming model for simple C++developers and a convenient concurrency control scheme,allowing managing different vendors of devices at the same time.Hence there is an opportunity to utilize oneAPI to design a general cross-architecture framework for large-scale graph computing.In this paper,we propose a large-scale graph computing framework for multiple types of accelerators with Intel oneAPI and we name it as OneGraph.Our approach signifi-cantly reduces the data transfer between GPU and CPU and masks the latency by asynchronous transfer,which significantly improves performance.We conducted rigorous performance tests on the framework using four classical graph algorithms.The experiment results show that our approach achieves an average speedup of 3.3x over the state-of-the-art partitioning-based approaches.Moreover,thanks to the cross-architecture model of Intel oneAPI,the framework can be deployed on different GPU platforms without code modification.And our evaluation proves that OneGraph has only less than 1%performance loss compared to the dedicated programming model on GPUs in large-scale graph computing. 展开更多
关键词 Heterogeneous programming Graph computing Out-of-memory process Cross-architecture portability OneAPI
基于DPCT的序列比对软件迁移与性能评估 认领 引用
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作者 李沛桢 张洋 陈文波 《计算机工程与科学》 CSCD 北大核心 2024年第8期1372-1380,共9页
利用GASAL2序列比对软件探索CUDA程序迁移到DPC++的过程。迁移过程中利用DPCT工具自动将CUDA API转换至DPC++API。然而,迁移后的代码仍需经过适配和修改才能正确编译和运行。评估DPCT工具从CUDA程序迁移到DPC++程序的有效性,并展示DPC+... 利用GASAL2序列比对软件探索CUDA程序迁移到DPC++的过程。迁移过程中利用DPCT工具自动将CUDA API转换至DPC++API。然而,迁移后的代码仍需经过适配和修改才能正确编译和运行。评估DPCT工具从CUDA程序迁移到DPC++程序的有效性,并展示DPC++在不同架构下的高效性。实验证明迁移后的程序保持了原始程序的精确度,且无需代码修改便可在异构设备Intel GPU架构上运行,同时迁移后的基于DPC++的GASAL2异构计算性能可以达到原始基于CUDA GASAL2的计算性能的大约90%~95%,充分展现了DPC++异构编程的可行性,为跨平台异构编程充分利用更广泛的硬件支持提供了有前景的解决方案。 展开更多
关键词 异构计算 oneAPI 生物信息学 序列比对
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