Warawa, JohnathanChester, Sean2026-04-202026-04-202026https://hdl.handle.net/1828/23644Matrix multiplication is a fundamental operation used to train neural networks for machine learning. GPUs are well-optimized for several stages of this operation and are thus used to accelerate the work, however, GPUs must be "hosted" by CPUs that remain underutilized while the GPU works, burning cycles that could be put to use by a more sophisticated, heterogeneous algorithm that makes use of both the GPU and CPU at the same time. In this project, we increase the speed of these operations beyond what a single processor could accomplish by developing a heterogeneous algorithm which efficiently divides and interleaves these operations.enGPUSIMDheterogeneous computingGEMMlinear algebratensor coresJamie Cassels Undergraduate Research Awards (JCURA)PosterDepartment of Computer Science