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Multi-block/multi-core SSOR preconditioner for the QCD quark solver for K computer

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 Added by Ken-Ichi Ishikawa
 Publication date 2012
  fields Physics
and research's language is English




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We study the algorithmic optimization and performance tuning of the Lattice QCD clover-fermion solver for the K computer. We implement the Luschers SAP preconditioner with sub-blocking in which the lattice block in a node is further divided to several sub-blocks to extract enough parallelism for the 8-core CPU SPARC64$^{mathrm{TM}}$ VIIIfx of the K computer. To achieve a better convergence property we use the symmetric successive over-relaxation (SSOR) iteration with {it locally-lexicographical} ordering for the sub-blocks in obtaining the block inverse. The SAP preconditioner is included in the single precision BiCGStab solver of the nested BiCGStab solver. The single precision part of the computational kernel are solely written with the SIMD oriented intrinsics to achieve the best performance of the SPARC on the K computer. We benchmark the single precision BiCGStab solver on the three lattice sizes: $12^3times 24$, $24^3times 48$ and $48^3times 96$, with fixing the local lattice size in a node at $6^3times 12$. We observe an ideal weak-scaling performance from 16 nodes to 4096 nodes. The performance of a computational kernel exceeds 50% efficiency, and the single precision BiCGstab has $sim26% susutained efficiency.



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We port Domain-Decomposed-alpha-AMG solver to the K computer. The system has 8 cores and 16 GB memory per node, of which theoretical peak is 128 GFlops (82,944 nodes in total). Its feature, as many as 256 registers per core and as large as 0.5 byte/Flop ratio, requires a different tuning from other machines. In order to use more registers, we change some of the data structure and rewrite matrix-vector operations with intrinsics. The performance is improved by more than a factor two for twelve solves including the setup. The efficiency is still about 5% after the optimization, which is lower than a previously tuned mixed precision solver for the K computer, 22%. The throughput is, however, more than two times better for a physical point configuration.
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