通过将快速傅里叶变换卸载到FPGA实现高效的从头算分子动力学模拟

Evaluation of Automatic GPU and FPGA Offloading for Function Blocks of Applications

In the recent years, systems using FPGAs, GPUs have increased due to their advantages such as power efficiency compared to CPUs. However, use in systems such as FPGAs and GPUs requires understanding hardware-specific technical specifications such as HDL and CUDA, which is a high hurdle. Based on this background, I previously proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code according to the hardware to be placed. As an element of the concept, I proposed a method to automatically offload loop statements o…

Proposal of Automatic FPGA Offloading for Applications Loop Statements

In recent years, with the prediction of Moore's law slowing down, utilization of hardware other than CPU such as FPGA which is energy effective is increasing. However, when using heterogeneous hardware other than CPUs, barriers of technical skills such as OpenCL are high. Based on that, I have proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code, according to the hardware to be placed. Partly of the offloading to the GPU was automated previously. In this paper, I propose and evaluate an automatic extraction…

Evaluation of Automatic FPGA Offloading for Loop Statements of Applications

In recent years, with the prediction of Moore's law slowing down, utilization of hardware other than CPU such as FPGA which is energy effective is increasing. However, when using heterogeneous hardware other than CPUs, barriers of technical skills such as OpenCL are high. Based on that, I have proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code, according to the hardware to be placed. Partly of the offloading to the GPU was automated previously. In this paper, I propose and evaluate an automatic extraction…

Study of Automatic Offloading Method in Mixed Offloading Destination Environment

In recent years, utilization of heterogeneous hardware other than small core CPU such as GPU, FPGA or many core CPU is increasing. However, when using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL are high. Based on that, I have proposed environment-adaptive software that enables automatic conversion, configuration, and high performance operation of once written code, according to the hardware to be placed. However, including existing technologies, there has been no research to properly and automatically offload the mixed offloading destination environmen…

通过OpenMP将Fortran卸载到FPGA的MLIR流水线

An MLIR pipeline for offloading Fortran to FPGAs via OpenMP

针对摩尔定律放缓下FPGA加速HPC工作负载的需求，首次在MLIR中实现通过OpenMP target指令将选择性代码卸载到FPGA的方案，结合OpenMP与HLS方言提供可移植编译流程，支持MLIR兼容前端，减少开发工作量并展示生态系统组合性优势。

FPGA上的透明实时代码卸载

Transparent Live Code Offloading on FPGA

尽管FPGA似乎终于从研究实验室过渡到消费类设备市场，但对其进行编程仍然具有挑战性。尽管高级综合（HLS）做出了改进，消除了阻止许多计算机科学家使用它们的语言和范式障碍，但产生一个新的设计通常需要至少几个小时，使得数据和上下文相关的适应几乎是不可能的。在本文中，我们提出了一个新的框架，卸载，在飞行和透明的用户和开发人员，计算密集型代码片段的FPGA。虽然性能不应该超过手工制作的HDL代码，甚至HLS生成的代码，但我们的结果没有额外的开发成本，并且每次更改时都不需要为FPGA生成和部署新的位流。此外，由于优化是在运行时进行的，因此它们可能适合特定的数据集或使用场景，这在设计或编译时很少可以预见。我们的建议围绕着一个覆盖架构，是预编程的FPGA和动态重新配置我们的框架执行代码片段提取的数据流图（DFG）的计算密集型例程。我们使用标准基准测试验证了我们的解决方案，并证明我们能够在没有开发人员干预的情况下将负载卸载到FPGA。