2,699 research outputs found
Scheduling data flow program in xkaapi: A new affinity based Algorithm for Heterogeneous Architectures
Efficient implementations of parallel applications on heterogeneous hybrid
architectures require a careful balance between computations and communications
with accelerator devices. Even if most of the communication time can be
overlapped by computations, it is essential to reduce the total volume of
communicated data. The literature therefore abounds with ad-hoc methods to
reach that balance, but that are architecture and application dependent. We
propose here a generic mechanism to automatically optimize the scheduling
between CPUs and GPUs, and compare two strategies within this mechanism: the
classical Heterogeneous Earliest Finish Time (HEFT) algorithm and our new,
parametrized, Distributed Affinity Dual Approximation algorithm (DADA), which
consists in grouping the tasks by affinity before running a fast dual
approximation. We ran experiments on a heterogeneous parallel machine with six
CPU cores and eight NVIDIA Fermi GPUs. Three standard dense linear algebra
kernels from the PLASMA library have been ported on top of the Xkaapi runtime.
We report their performances. It results that HEFT and DADA perform well for
various experimental conditions, but that DADA performs better for larger
systems and number of GPUs, and, in most cases, generates much lower data
transfers than HEFT to achieve the same performance
Locality-aware parallel block-sparse matrix-matrix multiplication using the Chunks and Tasks programming model
We present a method for parallel block-sparse matrix-matrix multiplication on
distributed memory clusters. By using a quadtree matrix representation, data
locality is exploited without prior information about the matrix sparsity
pattern. A distributed quadtree matrix representation is straightforward to
implement due to our recent development of the Chunks and Tasks programming
model [Parallel Comput. 40, 328 (2014)]. The quadtree representation combined
with the Chunks and Tasks model leads to favorable weak and strong scaling of
the communication cost with the number of processes, as shown both
theoretically and in numerical experiments.
Matrices are represented by sparse quadtrees of chunk objects. The leaves in
the hierarchy are block-sparse submatrices. Sparsity is dynamically detected by
the matrix library and may occur at any level in the hierarchy and/or within
the submatrix leaves. In case graphics processing units (GPUs) are available,
both CPUs and GPUs are used for leaf-level multiplication work, thus making use
of the full computing capacity of each node.
The performance is evaluated for matrices with different sparsity structures,
including examples from electronic structure calculations. Compared to methods
that do not exploit data locality, our locality-aware approach reduces
communication significantly, achieving essentially constant communication per
node in weak scaling tests.Comment: 35 pages, 14 figure
LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing
LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft
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