40,435 research outputs found
HSTREAM: A directive-based language extension for heterogeneous stream computing
Big data streaming applications require utilization of heterogeneous parallel
computing systems, which may comprise multiple multi-core CPUs and many-core
accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such
systems require advanced knowledge of several hardware architectures and
device-specific programming models, including OpenMP and CUDA. In this paper,
we present HSTREAM, a compiler directive-based language extension to support
programming stream computing applications for heterogeneous parallel computing
systems. HSTREAM source-to-source compiler aims to increase the programming
productivity by enabling programmers to annotate the parallel regions for
heterogeneous execution and generate target specific code. The HSTREAM runtime
automatically distributes the workload across CPUs and accelerating devices. We
demonstrate the usefulness of HSTREAM language extension with various
applications from the STREAM benchmark. Experimental evaluation results show
that HSTREAM can keep the same programming simplicity as OpenMP, and the
generated code can deliver performance beyond what CPUs-only and GPUs-only
executions can deliver.Comment: Preprint, 21st IEEE International Conference on Computational Science
and Engineering (CSE 2018
A hierarchic task-based programming model for distributed heterogeneous computing
Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Clouds introducing in its computing nodes, processors with different core architectures, accelerators (i.e. GPUs, FPGAs), as well as different memories and storage devices in order to achieve better performance with lower energy consumption. As a consequence of this heterogeneity, programming applications for these distributed heterogeneous platforms becomes a complex task. Additionally to the complexity of developing an application for distributed platforms, developers must also deal now with the complexity of the different computing devices inside the node. In this article, we present a programming model that aims to facilitate the development and execution of applications in current and future distributed heterogeneous parallel architectures. This programming model is based on the hierarchical composition of the COMP Superscalar and Omp Superscalar programming models that allow developers to implement infrastructure-agnostic applications. The underlying runtime enables applications to adapt to the infrastructure without the need of maintaining different versions of the code. Our programming model proposal has been evaluated on real platforms, in terms of heterogeneous resource usage, performance and adaptation.This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program
under contract 687584 (TANGO project) by the Spanish Government under contract TIN2015-65316 and grant SEV-2015-0493 (Severo Ochoa Program) and by Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272.Peer ReviewedPostprint (author's final draft
Towards an Energy-Aware Framework for Application Development and Execution in Heterogeneous Parallel Architectures
The Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation (TANGO) project’s goal is to characterise factors which affect power consumption in software development and operation for Heterogeneous Parallel Hardware (HPA) environments. Its main contribution is the combination of requirements engineering and design modelling for self-adaptive software systems, with power consumption awareness in relation to these environments. The energy efficiency and application quality factors are integrated into the application lifecycle (design, implementation and operation). To support this, the key novelty of the project is a reference architecture and its implementation. Moreover, a programming model with built-in support for various hardware architectures including heterogeneous clusters, heterogeneous chips and programmable logic devices is provided. This leads to a new cross-layer programming approach for heterogeneous parallel hardware architectures featuring software and hardware modelling. Application power consumption and performance, data location and time-criticality optimization, as well as security and dependability requirements on the target hardware architecture are supported by the architecture
Toward optimised skeletons for heterogeneous parallel architecture with performance cost model
High performance architectures are increasingly heterogeneous with shared and
distributed memory components, and accelerators like GPUs. Programming such
architectures is complicated and performance portability is a major issue as the
architectures evolve. This thesis explores the potential for algorithmic skeletons
integrating a dynamically parametrised static cost model, to deliver portable
performance for mostly regular data parallel programs on heterogeneous archi-
tectures.
The rst contribution of this thesis is to address the challenges of program-
ming heterogeneous architectures by providing two skeleton-based programming
libraries: i.e. HWSkel for heterogeneous multicore clusters and GPU-HWSkel
that enables GPUs to be exploited as general purpose multi-processor devices.
Both libraries provide heterogeneous data parallel algorithmic skeletons including
hMap, hMapAll, hReduce, hMapReduce, and hMapReduceAll.
The second contribution is the development of cost models for workload dis-
tribution. First, we construct an architectural cost model (CM1) to optimise
overall processing time for HWSkel heterogeneous skeletons on a heterogeneous
system composed of networks of arbitrary numbers of nodes, each with an ar-
bitrary number of cores sharing arbitrary amounts of memory. The cost model
characterises the components of the architecture by the number of cores, clock
speed, and crucially the size of the L2 cache. Second, we extend the HWSkel cost
model (CM1) to account for GPU performance. The extended cost model (CM2)
is used in the GPU-HWSkel library to automatically nd a good distribution
for both a single heterogeneous multicore/GPU node, and clusters of heteroge-
neous multicore/GPU nodes. Experiments are carried out on three heterogeneous
multicore clusters, four heterogeneous multicore/GPU clusters, and three single
heterogeneous multicore/GPU nodes. The results of experimental evaluations for
four data parallel benchmarks, i.e. sumEuler, Image matching, Fibonacci, and
Matrix Multiplication, show that our combined heterogeneous skeletons and cost
models can make good use of resources in heterogeneous systems. Moreover using
cores together with a GPU in the same host can deliver good performance either
on a single node or on multiple node architectures
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