HeteroPBLAS: A Set of Parallel Basic Linear Algebra Subprograms Optimized for Heterogeneous Computational Clusters

Ravi Reddy, Alexey Lastovetsky, Pedro Alonso


This paper presents a software library, called Heterogeneous PBLAS (HeteroPBLAS), which provides optimized parallel basic linear algebra subprograms for Heterogeneous Computational Clusters. This library is written on the top of HeteroMPI and PBLAS whose building blocks, the de facto standard kernels for matrix and vector operations (BLAS) and message passing communication (BLACS), are optimized for heterogeneous computational clusters. This is the first step towards the development of a parallel linear algebra package for Heterogeneous Computational Clusters.

We show that the efficiency of the parallel routines is due to the most important feature of the library, which is the automation of the difficult optimization tasks of parallel programming on heterogeneous computing clusters. They are the determination of the accurate values of the platform parameters such as the speeds of the processors and the latencies and bandwidths of the communication links connecting different pairs of processors, the optimal values of the algorithmic parameters such as the data distribution blocking factor, the total number of processes, the 2D process grid arrangement, and the efficient mapping of the processes executing the parallel algorithm to the executing nodes of the heterogeneous computing cluster.

We present the user interface and the software hierarchy of the first research implementation of HeteroPBLAS. We demonstrate the efficiency of the HeteroPBLAS programs on a homogeneous computing cluster and a heterogeneous computing cluster.


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