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A scalable high performant cholesky factorization for multicore with GPU accelerators.” LAPACK Working Note (2009)
| Content Provider | CiteSeerX |
|---|---|
| Author | Ltaief, Hatem Tomov, Stanimire Nath, Rajib Du, Peng Dongarra, Jack |
| Abstract | Abstract. We present a Cholesky factorization for multicore with GPU accelerators. The challenges in developing scalable high performance algorithms for these emerging systems stem from their heterogeneity, massive parallelism, and the huge gap between the GPUs ’ compute power vs the CPU-GPU communication speed. We show an approach that is largely based on software infrastructures that have already been developed for homogeneous multicores and hybrid GPU-based computing. The algorithm features two levels of nested parallelism. A coarse-grained parallelism is provided by splitting the computation into tiles for concurrent execution between GPUs. A fine-grained parallelism is further provided by splitting the work-load within a tile for high efficiency computing on GPUs but also, in certain cases, to benefit from hybrid computations by using both GPUs and CPUs. Our resulting computational kernels are highly optimized. An efficient task scheduling mechanism ensures a load balanced execution over the entire multicore with GPU |
| File Format | |
| Language | English |
| Publisher Date | 2009-01-01 |
| Access Restriction | Open |
| Subject Keyword | Gpu Accelerator Lapack Working Note Scalable High Performant Cholesky Factorization Hybrid Gpu-based Computing Coarse-grained Parallelism High Efficiency Cpu-gpu Communication Speed Efficient Task Entire Multicore Huge Gap Massive Parallelism Cholesky Factorization Algorithm Feature Computational Kernel Software Infrastructure Homogeneous Multicores Hybrid Computation Fine-grained Parallelism Concurrent Execution Scalable High Performance Algorithm Certain Case Gpus Compute Power Load Balanced Execution Nested Parallelism |
| Content Type | Text |
| Resource Type | Technical Report |
