Technologies for optimizing sparse matrix code with field-programmable gate arrays

摘要

Technologies for optimizing sparse matrix code include a target computing device having a processor and a field-programmable gate array (FPGA). A compiler identifies a performance-critical loop in a sparse matrix source code and generates optimized executable code, including processor code and FPGA code. The target computing device executes the optimized executable code, using the processor for the processor code and the FPGA for the FPGA code. The processor executes a first iteration of the loop, generates reusable optimization data in response to executing the first iteration, and stores the reusable optimization data in a shared memory. The FPGA accesses the optimization data in the shared memory, executes additional iterations of the loop, and optimizes the additional iterations of the loop based on the optimization data. The optimization data may include, for example, loop-invariant data, reordered data, or alternate data storage representations. Other embodiments are described and claimed.