Xabclib:A Fully Auto-tuned Sparse Iterative Solver
CoRR(2024)
Abstract
In this paper, we propose a general application programming interface named
OpenATLib for auto-tuning (AT). OpenATLib is designed to establish the
reusability of AT functions. By using OpenATLib, we develop a fully auto-tuned
sparse iterative solver named Xabclib. Xabclib has several novel run-time AT
functions. First, the following new implementations of sparse matrix-vector
multiplication (SpMV) for thread processing are implemented:(1) non-zero
elements; (2) omission of zero-elements computation for vector reduction; (3)
branchless segmented scan (BSS). According to the performance evaluation and
the comparison with conventional implementations, the following results are
obtained: (1) 14x speedup for non-zero elements and zero-elements computation
omission for symmetric SpMV; (2) 4.62x speedup by using BSS. We also develop a
"numerical computation policy" that can optimize memory space and computational
accuracy. Using the policy, we obtain the following: (1) an averaged 1/45
memory space reduction; (2) avoidance of the "fault convergence" situation,
which is a problem of conventional solvers.
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