Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL

نویسندگان

  • Hamid Reza Zohouri
  • Artur Podobas
  • Satoshi Matsuoka
چکیده

Recent developments in High Level Synthesis tools have attracted software programmers to accelerate their high-performance computing applications on FPGAs. Even though it has been shown that FPGAs can compete with GPUs in terms of performance for stencil computation, most previous work achieve this by avoiding spatial blocking and restricting input dimensions relative to FPGA on-chip memory. In this work we create a stencil accelerator using Intel FPGA SDK for OpenCL that achieves high performance without having such restrictions. We combine spatial and temporal blocking to avoid input size restrictions, and employ multiple FPGA-specific optimizations to tackle issues arisen from the added design complexity. Accelerator parameter tuning is guided by our performance model, which we also use to project performance for the upcoming Intel Stratix 10 devices. On an Arria 10 GX 1150 device, our accelerator can reach up to 760 and 375 GFLOP/s of compute performance, for 2D and 3D stencils, respectively, which rivals the performance of a highly-optimized GPU implementation. Furthermore, we estimate that the upcoming Stratix 10 devices can achieve a performance of up to 3.5 TFLOP/s and 1.6 TFLOP/s for 2D and 3D stencil computation, respectively.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

High-Level Programming of Stencil Computations on Multi-GPU Systems Using the SkelCL Library

The implementation of stencil computations on modern, massively parallel systems with GPUs and other accelerators currently relies on manually-tuned coding using low-level approaches like OpenCL and CUDA. This makes development of stencil applications a complex, time-consuming, and error-prone task. We describe how stencil computations can be programmed in our SkelCL approach that combines high...

متن کامل

Extending the SkelCL Skeleton Library for Stencil Computations on Multi-GPU Systems

The implementation of stencil computations on modern, massively parallel systems with GPUs and other accelerators currently relies on manually-tuned coding using low-level approaches like OpenCL and CUDA, which makes it a complex, time-consuming, and error-prone task. We describe how stencil computations can be programmed in our SkelCL approach that combines high level of programming abstractio...

متن کامل

OpenCL for FPGAs: Prototyping a Compiler

Hardware acceleration using FPGAs has shown orders of magnitude reduction in runtime of computationally-intensive applications in comparison to traditional stand-alone computers [1]. This is possible because on an FPGA many computations can be performed at the same time in a trulyparallel fashion. However, parallel computation at a hardware level requires a great deal of expertise, which limits...

متن کامل

Algorithmic Trading: A brief, computational finance case study on data centre FPGAs

Increasingly FPGAs will be deployed at scale as a result of the need for increased power efficient computation and improved high level synthesis tool flows, creating a new category of device: data centre FPGAs. A method for using these FPGAs is to identify what proportion of a given workload would benefit from being implemented upon the available FPGAs while minimising communication off-chip. A...

متن کامل

An Auto-tuning Jit Compiler for Accelerating Multiple Stencil Computations

We present a JIT compiler with auto-tuning capabilities fusing multiple stencil computations. Data arrays for scientific computing of image processing often exceed cache-memory size. To take advantage of spatial and temporal locality, a common method is to partition the images into tiling blocks for multicore architectures. In realistic scenarios, the multiple image algorithms, most of which ar...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2018