Analyzing the Performance Portability of Tensor Decomposition
We employ pressure point analysis and roofline modeling to identify performance bottlenecks and determine an upper bound on the performance of the Canonical Polyadic Alternating Poisson Regression Multiplicative Update (CP-APR MU) algorithm in the SparTen software library. Our analyses reveal that a...
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Zusammenfassung: | We employ pressure point analysis and roofline modeling to identify
performance bottlenecks and determine an upper bound on the performance of the
Canonical Polyadic Alternating Poisson Regression Multiplicative Update (CP-APR
MU) algorithm in the SparTen software library. Our analyses reveal that a
particular matrix computation, $\Phi^{(n)}$, is the critical performance
bottleneck in the SparTen CP-APR MU implementation. Moreover, we find that
atomic operations are not a critical bottleneck while higher cache reuse can
provide a non-trivial performance improvement. We also utilize grid search on
the Kokkos library parallel policy parameters to achieve 2.25x average speedup
over the SparTen default for $\Phi^{(n)}$ computation on CPU and 1.70x on GPU.
We conclude our investigations by comparing Kokkos implementations of the
STREAM benchmark and the matricized tensor times Khatri-Rao product (MTTKRP)
benchmark from the Parallel Sparse Tensor Algorithm (PASTA) benchmark suite to
implementations using vendor libraries. We show that with a single
implementation Kokkos achieves performance comparable to hand-tuned code for
fundamental operations that make up tensor decomposition kernels on a wide
range of CPU and GPU systems. Overall, we conclude that Kokkos demonstrates
good performance portability for simple data-intensive operations but requires
tuning for algorithms with more complex dependencies and data access patterns. |
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DOI: | 10.48550/arxiv.2307.03276 |