State-of-the-art in Heterogeneous Computing

Brodtkorb, André R.; Dyken, Christopher; Hagen, Trond Runar; Hjelmervik, Jon M.; Storaasli, Olaf O.

doi:10.1155/2010/540159

Cited by 188 publications

(143 citation statements)

References 123 publications

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“…P. ENGSIG-KARUP, M. G. MADSEN AND S. L. GLIMBERG significantly, the last few years, and the state-of-the-art is rapidly developing, for example, see [12][13][14]. Main reasons for this successful turnaround are that these accelerator devices are designed as special purpose co-processors for the use with current general purpose CPUs, they are programmable, mass produced, and therefore offered at affordable prices and already present in many machines.…”

mentioning

confidence: 99%

A massively parallel GPU‐accelerated model for analysis of fully nonlinear free surface waves

Engsig-Karup

Madsen

Glimberg

2011

Numerical Methods in Fluids

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SUMMARYWe implement and evaluate a massively parallel and scalable algorithm based on a multigrid preconditioned Defect Correction method for the simulation of fully nonlinear free surface flows. The simulations are based on a potential model that describes wave propagation over uneven bottoms in three space dimensions and is useful for fast analysis and prediction purposes in coastal and offshore engineering. A dedicated numerical model based on the proposed algorithm is executed in parallel by utilizing affordable modern special purpose graphics processing unit (GPU). The model is based on a low-storage flexible-order accurate finite difference method that is known to be efficient and scalable on a CPU core (single thread). To achieve parallel performance of the relatively complex numerical model, we investigate a new trend in high-performance computing where many-core GPUs are utilized as high-throughput co-processors to the CPU. We describe and demonstrate how this approach makes it possible to do fast desktop computations for large nonlinear wave problems in numerical wave tanks (NWTs) with close to 50/100 million total grid points in double/single precision with 4 GB global device memory available. A new code base has been developed in C++ and compute unified device architecture C and is found to improve the runtime more than an order in magnitude in double precision arithmetic for the same accuracy over an existing CPU (single thread) Fortran 90 code when executed on a single modern GPU. These significant improvements are achieved by carefully implementing the algorithm to minimize data-transfer and take advantage of the massive multi-threading capability of the GPU device.

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mentioning

confidence: 99%

A massively parallel GPU‐accelerated model for analysis of fully nonlinear free surface waves

Engsig-Karup

Madsen

Glimberg

2011

Numerical Methods in Fluids

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“…Heterogeneous computing systems (HCS) are composed by hybrid collections of processors (frequently GPUs and CPUs) in the same system [41], and often in the same chip. This trend is intended to satisfy the computational needs of every workload.…”

Section: Heterogeneous Parallel Computingmentioning

confidence: 99%

High-Performance Computation of Bézier Surfaces on Parallel and Heterogeneous Platforms

Palomar

Gómez-Luna

Cheikh³

et al. 2017

Int J Parallel Prog

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Bézier surfaces are mathematical tools employed in a wide variety of applications. Some works in the literature propose parallelization strategies to improve performance for the computation of Bézier surfaces. These approaches, however, are mainly focused on graphics applications and often are not directly applicable to other domains. In this work, we propose a new method for the computation of Bézier surfaces, together with approaches to efficiently map the method onto different platforms (CPUs, discrete and integrated GPUs). Additionally, we explore CPU-GPU cooperation mechanisms for computing Bézier surfaces using two integrated heterogeneous systems with different characteristics. An exhaustive performance evaluation-including different data-types, rendering and several hardware platforms-is performed. The results show that our method achieves speedups as high as 3.12x (double-precision) and 2.47x (single-precision) on CPU, and 3.69x (double-precision) and 13.14x (single-precision) on GPU compared to other methods in the literature. In heterogeneous platforms, the CPU-GPU cooperation increases the performance up to 2.09x with respect to the GPU-only version. Our method and the 123Int J Parallel Prog associated parallelization approaches can be easily employed in domains other than computer-graphics (e.g., image registration, bio-mechanical modeling and flow simulation), and extended to other Bézier formulations and Bézier constructions of higher order than surfaces.

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“…Heterogeneous computing aims to combine the parallelism of traditional multicore CPUs and GPU accelerator cores to deliver unprecedented levels of performance [5]. While the phrase typically refers to a single node, a distributed environment may be constructed from such heterogeneous nodes.…”

Section: Distributed Computing In a Heterogeneous Environmentmentioning

confidence: 99%

Accelerating text mining workloads in a MapReduce-based distributed GPU environment

Wittek

Daranyi

2013

Journal of Parallel and Distributed Computing

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Scientific computations have been using GPU-enabled computers successfully, often relying on distributed nodes to overcome the limitations of device memory. Only a handful of text mining applications benefit from such infrastructure. Since the initial steps of text mining are typically data-intensive, and the ease of deployment of algorithms is an important factor in developing advanced applications, we introduce a flexible, distributed, MapReducebased text mining workflow that performs I/O-bound operations on CPUs with industry-standard tools and then runs compute-bound operations on GPUs which are optimized to ensure coalesced memory access and effective use of shared memory. We have performed extensive tests of our algorithms on a cluster of eight nodes with two NVidia Tesla M2050 attached to each, and we achieve considerable speedups for random projection and self-organizing maps.

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State-of-the-art in Heterogeneous Computing

Cited by 188 publications

References 123 publications

A massively parallel GPU‐accelerated model for analysis of fully nonlinear free surface waves

A massively parallel GPU‐accelerated model for analysis of fully nonlinear free surface waves

High-Performance Computation of Bézier Surfaces on Parallel and Heterogeneous Platforms

Accelerating text mining workloads in a MapReduce-based distributed GPU environment

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