可扩展的OpenCL FPGA计算进化

A. Vassiliev
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引用次数: 1

摘要

多fpga加速已经在当今的分析、科学、金融、图像处理和机器学习应用中显示出几个数量级的加速。然而,未来数据和网络流量雪崩所要求的基于fpga的高性能计算的可扩展性仍处于起步阶段。OpenCL内存模型承诺在当今的OpenCL FPGA编译器框架内超越单个FPGA的FPGA计算的直接和实用的可扩展性。科学概念国际公司开发了新颖的智能细胞互连(SCI),优化了OpenCL全局内存,流数据访问和封装在交换细胞中的网络数据包。基于多面体模型和IR的开源工具的最新进展使得可以开发源到源的粗粒度代码和HPC工作负载的数据分区,这些工作负载最初是用OpenCL编写的,随后是C/ c++, SYCL。多fpga分区工具和SCI互连IP的发展将使fpga阵列和集群的计算结构具有真正的可扩展性。我们的架构、分区工具和未来的产品路线图将解决云FPGA计算、生成数据源的雾计算以及网络、安全性和计算的融合问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable OpenCL FPGA Computing Evolution
Multi-FPGA acceleration has already shown several orders of magnitude speedups in today's analytics, scientific, financial, image processing and machine learning applications. However, the scalability of FPGA-based HPC demanded by the future avalanche of data and network traffic is still in its infancy. OpenCL memory model holds the promise of straightforward and pragmatic scalability of FPGA computing beyond single FPGA within the framework of today's OpenCL FPGA compilers. Scientific Concepts International develops novel Smart Cell Interconnect (SCI) optimized for OpenCL global memory, streaming data accesses and network packets encapsulated into switched cells. Recent advances in Open Source tools based on polyhedral model and IR enable development of source-to-source coarse grain code and data partitioning of the HPC workloads written initially in OpenCL and followed by C/C++, SYCL. Evolution of the multi-FPGA partitioning tools and SCI interconnect IP will enable true scalability of the computing fabric of arrays and clusters of FPGAs. Cloud FPGA computing, fog computing at the source of the generated data as well as fusion of networking, security, and computing are addressed by our architecture, partitioner tools, and future product roadmap.
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