Fast Kronecker Matrix-Matrix Multiplication on GPUs
Abhinav Jangda, Mohit Yadav
摘要
Kronecker Matrix-Matrix Multiplication (Kron-Matmul) is the multiplication of a matrix with the Kronecker Product of several smaller matrices. Kron-Matmul is a core operation for many scientific and machine learning computations. State-of-the-art Kron-Matmul implementations utilize existing tensor algebra operations, such as matrix multiplication, transpose, and tensor matrix multiplication. However, this design choice prevents several Kron-Matmul specific optimizations, thus, leaving significant performance on the table.
To address this issue, we present FastKron, an efficient technique for Kron-Matmul on single and multiple GPUs. FastKron is independent of linear algebra operations enabling several new optimizations for Kron-Matmul. Thus, it performs up to 40.7× and 7.85× faster than existing implementations on 1 and 16 GPUs respectively.
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- Breaking the computation and communication abstraction barrier in distributed machine learning workloadsAbhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet 等ASPLOS 2022 · 被引用 68 次
- DISTAL: the distributed tensor algebra compilerRohan Yadav, Alex Aiken, Fredrik KjolstadPLDI 2022 · 被引用 29 次
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