Date of Award
Master of Science in Computer Engineering
Electrical, Computer, and Biomedical Engineering
Reaching the so-called “performance wall” in 2004 inspired innovative approaches to performance improvement. Parallel programming, distributive computing, and System on a Chip (SOC) design drove change. Hardware acceleration in mainstream computing systems brought significant improvement in the performance of applications targeted directly to a specific hardware platform. Targeting a single hardware platform, however, typically requires learning vendor and hardware-specific languages that can be very complex. Additionally, Heterogeneous Computing Environments (HCE) consist of multiple SOC hardware platforms, so why not use them all instead of just one? How do we communicate with all platforms while maximizing performance, decreasing memory latency, and conserving power consumption? Enter the Open Computing Language (OpenCL) which has been developed to harness the power and performance of multiple SOC devices in an HCE. OpenCL offers an alternative to learning vendor and hardware-specific languages while still being able to harness the power of each device. Thus far, OpenCL programming has been directly mostly at CPU and GPU hardware devices.
The genesis of this thesis is to examine the connections between parallel computing in a HCE using OpenCL with CPU and FPGA hardware devices. Underlining the industry trends to favor FPGAs in both computationally intensive and embedded systems, this research will also highlight the FPGA specifically demonstrating comparable performance ratings as CPU and GPU at a fraction of the power consumption. OpenCL benchmark suites run on a FPGA will show the importance of performance per watt and how it can be measured. Running traditional parallel programs will demonstrate the power and portability of the OpenCL language and how it can maximize performance of FPGA, CPU, and GPU. Results will show that OpenCL is a solid approach to exploiting all the computing power of a HCE and that performance per watt matters in mainstream computing systems, making a strong case for further research into using OpenCL with FPGAs in a HCE.
Hoffman, Herman Noel, "Program Acceleration in a Heterogeneous Computing Environment Using OpenCL, FPGA, and CPU" (2017). Open Access Master's Theses. Paper 1015.
Appendix A: Hello World
Appendix B Vector_Addition.cpp (10 kB)
Appendix B: Vector Addition
Appendix C Histogram.cpp (4 kB)
Appendix C: Histogram
Appendix D Black Sholes Original Code.cpp (2 kB)
Appendix D: Black Sholes Original Code
Appendix E Black Sholes OpenCL.cpp (5 kB)
Appendix E: Black Sholes OpenCL