12/29/2022 0 Comments Serial quark 2018The effectiveness of this approach for utilizing multi-way heterogeneous hardware resources is demonstrated by implementing dense linear algebra applications. The adaptation is done at multiple points, including multi-level data partitioning, adaptive task grain sizes, and dynamic task scheduling. We propose a productive programming model starting from serial code, which achieves parallelism and scalability by using a task-superscalar runtime environment to adapt the computation to the available resources. The domain scientist would have to design and schedule an application in multiple degrees of parallelism and task grain sizes in order to obtain efficient performance from the resources. For example, in order to use GPUs effectively, the workload must have a greater degree of parallelism than a workload designed for a multicore-CPU. The challenge faced by domain scientists is how to efficiently and productively use these varied resources. The GPU has been accelerated about 2.5–5.9 times during the numerical factorization step and approximately 1.9–4.7 times over the whole analysis process, compared to an experimental CPU device.Ī wide variety of heterogeneous compute resources are available to modern computers, including multiple sockets containing multicore CPUs, one-or-more GPUs of varying power, and coprocessors such as the Intel Xeon Phi. The computing time, memory usage, and solution accuracy are compared with those obtained from DSS included in MKL. Mechanical dynamic analysis has been carried out to show the efficiency of the proposed method. Numerical experiments show that the proposed method takes smaller factorization time than CHOLMOD in general and has better operational availability than SPQR. Separators are divided into smaller blocks to further improve the parallel efficiency. The proposed implementation allows solving large problems efficiently on many kinds of GPUs. In contrast to the DFS post-order traversal which is widely used for conventional implementation of supernodal and multifrontal methods, the BFS reverse-level order traversal has been adopted to obtain more parallelism and a more adaptive control of data size. The proposed solver is applied to mechanical system analysis. A new direct linear equation solver is proposed for GPUs.
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