In this way, groups composed by different number of problems are distributed on cores, achieving a more balanced distribution in terms of computational cost.

Also, we propose a new strategy called grouping to deal with batch variable, which are able to distribute no homogeneous bins of DGEMMs on cores, achieving a ...

A high-performance batched matrix multiplication framework for GPUs under unbalanced input distribution · Computer Science, Engineering. The Journal of ...

Abstract—Many scientific applications are in need to solve a high number of small-size independent problems. These individual problems do not provide enough ...

In this way, groups composed by different number of problems are distributed on cores, achieving a more balanced distribution in terms of computational cost.

Request PDF | On Mar 1, 2018, Pedro Valero-Lara and others published Variable Batched DGEMM | Find, read and cite all the research you need on ResearchGate.

In this paper we will make an experimental description of the parallel programming using OpenMP. Using OpenMP, we achieve a high performance parallelizing the ...

This section discusses the main design and tuning approaches for batched GEMM kernels that support both fixed and variable sizes. From now on, variable size.

May 20, 2019 · I have a problem where I need to compute many (1e4 - 1e6) small matrix-matrix and matrix-vector products (matrix dimensions around ~15 - 35).

Feb 27, 2017 · In this post, I detail solutions now available in cuBLAS 8.0 for batched matrix multiply and show how it can be applied to efficient tensor contractions.