Compiler-Based Tools to Aid in Data Transfer Optimization and On-Chip Debug of Heterogeneous Compute Systems
First, we present techniques to efficiently schedule data transfers through compiler analyses. Compared to transferring data immediately before and after the kernel executes, our scheduling results in orders of magnitude improvements in execution time, number of data transfers, and number of bytes t...
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| Format: | Others |
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BYU ScholarsArchive
2020
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| Online Access: | https://scholarsarchive.byu.edu/etd/8613 https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=9613&context=etd |
| Summary: | First, we present techniques to efficiently schedule data transfers through compiler analyses. Compared to transferring data immediately before and after the kernel executes, our scheduling results in orders of magnitude improvements in execution time, number of data transfers, and number of bytes transferred. Second, we demonstrate techniques to provide on-chip debugging for heterogeneous systems through recording execution on the software in addition to debugging circuitry in the hardware, and provide a temporal correlation between the hardware and software traces through synchronization. This allows us to follow debug data between the hardware and software trace buffers. Due to the added cost of synchronizing the trace buffers, we explore synchronization schemes which can reduce the impact synchronization depending on the code structure. We demonstrate the quantitative impact of these techniques on execution time and hardware and software resources, which are under a 2x increase to execution time in most cases. Third, we demonstrate how source-code debugging techniques for on-chip debugging can be applied to OpenCL FPGA kernels in heterogeneous systems. We developed techniques and a tool-flow that allows users to select variables to record, automatically insert recording instructions into the kernel source code, synthesize the changes directly into the hardware design using commercial HLS tools, retrieve the trace data through kernel arguments, and present it to the user. Overall, quantitative measurements showed our techniques resulted in modest increases to execution time and hardware resources. |
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