GSRC Student Profile:
Research Overview: A Predictive Model for Solving Small Linear Algebra Problems in GPU Registers
I am examining the problem of solving many thousands of small dense linear algebra factorizations simultaneously on Graphics Processing Units (GPUs). I am interested in problems ranging from several hundred of rows and columns to 4 × 4 matrices. Problems of this size are common, especially in signal processing. However, they have received very little attention from current numerical linear algebra libraries for GPUs, which have thus far focused only on very large problems found in traditional supercomputing applications and benchmarks. To solve small problems efficiently I tailor my implementation to the GPUs inverted memory hierarchy and multilevel parallelism hierarchy. I also have a model of the GPU memory subsystem that can accurately predict and explain the performance of this approach across different problem sizes.
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