Study of OpenCL processing models for FPGA device

Piotr Szkotak, Pawel Russek, Kazmierz Wiatr

Abstract


In our study, we present the results of the implementation of SHA-512 algorithm in FPGA. The distinguished element of our work is that we conducted the work using OpenCL for FPGA which is a relatively new development method for reconfigurable logic. We examine the loop unrolling; as the OpenCL performance optimisation method, and compare the efficiency of the different kernel implementation types: NDRange, Single-Work Item, and SIMD kernels. In conclusions, we compare metrics of the created FPGA accelerator to the corresponding GPGPU solutions. Also, our paper is accompanied by the source code repository to allow the reader to follow and extend our survey.

Keywords


OpenCL; recongurable computing; accelerated computing; high-level hardware synthesis

Full Text:

PDF

References


ACC Cyfronet AGH. http://www.cyfronet.pl/en/. Accessed: 2018-10-29.

Khronos Group. OpenCL overview. https://www.khronos.org/opencl/. Accessed: 2018-10-25.

Altera Corporation: Altera SDK for OpenCL Getting Started, Version 15.0.0, 2015.

Altera Corporation: Altera SDK for OpenCL Optimization Guide, Version 15.0.0, 2015.

Che S., Li J., Sheaer J.W., Skadron K., Lach J.: Accelerating Compute-Intensive Applications with GPUs and FPGAs. In: 2008 Symposium on Application Specifc Processors, pp. 101-107. 2008. URL [6] Ge C., Xu L., Qiu W., Huang Z., Guo J., Liu G., Gong Z.: Optimized Password Recovery for SHA-512 on GPUs. In: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC), vol. 2, pp. 226-229. 2017.

Hill K., Craciun S., George A., Lam H.: Comparative analysis of OpenCL vs. HDL with image-processing kernels on Stratix-V FPGA. In: 2015 IEEE 26th International Conference on Application-specic Systems, Architectures and Processors (ASAP), pp. 189-193. 2015.

Janik I., Khalid M.A.S.: Synthesis and evaluation of SHA-1 algorithm using altera SDK for OpenCL. In: 2016 IEEE 59th International Midwest Symposium on Circuits and Systems (MWSCAS), pp. 1-4. 2016.

Khronos OpenCL Working Group: The OpenCL Specication, Version 1.0, 2011.

National Institute of Standards and Technology: FIPS 180-2, Secure Hash Standard, Federal Information Processing Standard (FIPS), Publication 180-2. Tech. rep., 2002. URL http://csrc.nist.gov/publications/fips/fips180-2/

fips180-2withchangenotice.pdf.

Nvidia: Geforce GTX 1080 specication, 2018. URL https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080/.

Russek P.: Data-intensive processing on FPGAs. chap. 2.3, pp. 62-67. AGH University of Science and Technology Press, 2015.

Russek P., Wiatr K.: The enhancement of a computer system for sorting capabilities using FPGA custom architecture. In: Computing and Informatics, vol. 32(4), pp. 859-876, 2014.

Szkotak, P. and Russek, P. and Wiatr, K.: SHA512AOCLStudy repository, 2018. URL https://git.plgrid.pl/scm/~plgrussek/sha512aoclstudy.git.

Tucci L.D., O'Brien K., Blott M., Santambrogio M.D.: Architectural optimizations for high performance and energy ecient Smith-Waterman implementation on FPGAs using OpenCL. In: Design, Automation Test in Europe Conference Exhibition (DATE), 2017, pp. 716-721. 2017.

Wang Z., He B., Zhang W., Jiang S.: A performance analysis framework for optimizing OpenCL applications on FPGAs. In: 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 114-125. 2016.

Wielgosz M., Mazur G., Makowski M., Jamro E., Russek P., Wiatr K.: Analysis of the Basic Implementation Aspects of Hardware-Accelerated Density Functional Theory Calculations. In: Computing and Informatics, vol. 29(6), pp. 989-1000, 2012.

Zohouri H.R., Maruyama N., Smith A., Matsuda M., Matsuoka S.: Evaluating and Optimizing OpenCL Kernels for High Performance Computing with FPGAs. In: SC '16: Proceedings of the International Conference for High Performance 2018/11/19; 16:38 str. 12/13

Computing, Networking, Storage and Analysis, pp. 409-420. 2016.




DOI: https://doi.org/10.7494/csci.2019.20.1.3114

Refbacks

  • There are currently no refbacks.