{"title":"CPU+GPU融合处理器(APU)并行计算效能研究","authors":"Mayank Daga, Ashwin M. Aji, Wu-chun Feng","doi":"10.1109/SAAHPC.2011.29","DOIUrl":null,"url":null,"abstract":"The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers between the CPU and GPU over PCIe. Emerging heterogeneous computing architectures that \"fuse\" the functionality of the CPU and GPU, e.g., AMD Fusion and Intel Knights Ferry, hold the promise of addressing the PCIe bottleneck. In this paper, we empirically characterize and analyze the efficacy of AMD Fusion, an architecture that combines general-purposex86 cores and programmable accelerator cores on the same silicon die. We characterize its performance via a set of micro-benchmarks (e.g., PCIe data transfer), kernel benchmarks(e.g., reduction), and actual applications (e.g., molecular dynamics). Depending on the benchmark, our results show that Fusion produces a 1.7 to 6.0-fold improvement in the data-transfer time, when compared to a discrete GPU. In turn, this improvement in data-transfer performance can significantly enhance application performance. For example, running a reduction benchmark on AMD Fusion with its mere 80 GPU cores improves performance by 3.5-fold over the discrete AMD Radeon HD 5870 GPU with its 1600 more powerful GPU cores.","PeriodicalId":331604,"journal":{"name":"2011 Symposium on Application Accelerators in High-Performance Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2011-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"138","resultStr":"{\"title\":\"On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing\",\"authors\":\"Mayank Daga, Ashwin M. Aji, Wu-chun Feng\",\"doi\":\"10.1109/SAAHPC.2011.29\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers between the CPU and GPU over PCIe. Emerging heterogeneous computing architectures that \\\"fuse\\\" the functionality of the CPU and GPU, e.g., AMD Fusion and Intel Knights Ferry, hold the promise of addressing the PCIe bottleneck. In this paper, we empirically characterize and analyze the efficacy of AMD Fusion, an architecture that combines general-purposex86 cores and programmable accelerator cores on the same silicon die. We characterize its performance via a set of micro-benchmarks (e.g., PCIe data transfer), kernel benchmarks(e.g., reduction), and actual applications (e.g., molecular dynamics). Depending on the benchmark, our results show that Fusion produces a 1.7 to 6.0-fold improvement in the data-transfer time, when compared to a discrete GPU. In turn, this improvement in data-transfer performance can significantly enhance application performance. For example, running a reduction benchmark on AMD Fusion with its mere 80 GPU cores improves performance by 3.5-fold over the discrete AMD Radeon HD 5870 GPU with its 1600 more powerful GPU cores.\",\"PeriodicalId\":331604,\"journal\":{\"name\":\"2011 Symposium on Application Accelerators in High-Performance Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"138\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 Symposium on Application Accelerators in High-Performance Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SAAHPC.2011.29\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 Symposium on Application Accelerators in High-Performance Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SAAHPC.2011.29","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 138
摘要
图形处理单元(GPU)作为并行计算的加速器已经取得了重大进展。然而,由于GPU作为一个独立的设备驻留在PCIe上,GPU应用程序的性能可能会受到CPU和GPU之间通过PCIe传输数据的瓶颈。新兴的异构计算架构“融合”了CPU和GPU的功能,例如AMD Fusion和Intel Knights Ferry,有望解决PCIe瓶颈问题。在本文中,我们对AMD Fusion的效能进行了实证表征和分析,这是一种将通用86内核和可编程加速器内核结合在同一硅片上的架构。我们通过一组微基准(例如PCIe数据传输)、内核基准(例如PCIe数据传输)来描述它的性能。(还原)和实际应用(如分子动力学)。根据基准测试,我们的结果表明,与独立GPU相比,Fusion在数据传输时间上提高了1.7到6.0倍。反过来,数据传输性能的改进可以显著提高应用程序性能。例如,在只有80个GPU核的AMD Fusion上运行缩减基准测试,性能比拥有1600个更强大GPU核的AMD Radeon HD 5870独立GPU提高3.5倍。
On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing
The graphics processing unit (GPU) has made significant strides as an accelerator in parallel computing. However, because the GPU has resided out on PCIe as a discrete device, the performance of GPU applications can be bottlenecked by data transfers between the CPU and GPU over PCIe. Emerging heterogeneous computing architectures that "fuse" the functionality of the CPU and GPU, e.g., AMD Fusion and Intel Knights Ferry, hold the promise of addressing the PCIe bottleneck. In this paper, we empirically characterize and analyze the efficacy of AMD Fusion, an architecture that combines general-purposex86 cores and programmable accelerator cores on the same silicon die. We characterize its performance via a set of micro-benchmarks (e.g., PCIe data transfer), kernel benchmarks(e.g., reduction), and actual applications (e.g., molecular dynamics). Depending on the benchmark, our results show that Fusion produces a 1.7 to 6.0-fold improvement in the data-transfer time, when compared to a discrete GPU. In turn, this improvement in data-transfer performance can significantly enhance application performance. For example, running a reduction benchmark on AMD Fusion with its mere 80 GPU cores improves performance by 3.5-fold over the discrete AMD Radeon HD 5870 GPU with its 1600 more powerful GPU cores.