Coordinating the use of GPU and CPU for improving performance of compute intensive applications

Teodoro, George; Sachetto, Rafael; Sertel, Olcay; Gürcan, Metin N.; Meira, Wagner; Çatalyürek, Ümit V.; Ferreira, Renato

doi:10.1109/clustr.2009.5289193

Cited by 70 publications

(42 citation statements)

References 14 publications

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“…In order to exploit this intra-filter task heterogeneity, we then proposed and implemented a task assignment policy, called demand-driven dynamic weighted round-robin [39] in the Anthill Event Scheduler module, previously shown in Sect. 3.…”

Section: Intra-filter Task Assignmentmentioning

confidence: 99%

“…Our approach assigns tasks to devices based on the relative performance of that device, with the aim of optimizing the overall execution time. While in previous work [39], we considered intranode parallelism, in the original version of this paper [37] and here we consider both intra-node and inter-node parallelism. We also present new techniques for on-line performance estimation and handling data transfers between the CPU and the GPU.…”

Section: Introductionmentioning

confidence: 98%

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Optimizing dataflow applications on heterogeneous environments

et al. 2011

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The increases in multi-core processor parallelism and in the flexibility of many-core accelerator processors, such as GPUs, have turned traditional SMP systems into hierarchical, heterogeneous computing environments. Fully exploiting these improvements in parallel system design remains an open problem. Moreover, most of the current tools for the development of parallel applications for hierarchical systems concentrate on the use of only a single processor type (e.g., accelerators) and do not coordinate several heterogeneous processors. Here, we show that making use of all of the heterogeneous computing resources can significantly improve application performance. Our approach, which consists of optimizing applications at run-time by efficiently coordinating application task execution on all available processing units is evaluated in the context of replicated dataflow applications. The proposed techniques were developed and implemented in an integrated run-time system targeting both intra-and inter-node parallelism. The experimental results with a real-world complex biomedical application show that our approach nearly doubles the performance of the GPU-only implementation on a distributed heterogeneous accelerator cluster.

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Section: Intra-filter Task Assignmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Optimizing dataflow applications on heterogeneous environments

et al. 2011

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“…We further embrace a coarser-grained data parallelism than StreamIt, which results in performance beyond prior work [28]. A number of other filter-based frameworks have been designed [29], [30], [31], [32], [33]. Similarly, they encapsulate computations into filters, a central concept to express algorithms.…”

Section: Related Workmentioning

confidence: 99%

GStream: A General-Purpose Data Streaming Framework on GPU Clusters

Zhang

Mueller

2011

2011 International Conference on Parallel Processing

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Abstract-Emerging accelerating architectures, such as GPUs, have proved successful in providing significant performance gains to various application domains. However, their viability to operate on general streaming data is still ambiguous. In this paper, we propose GStream, a general-purpose, scalable data streaming framework on GPUs. The contributions of GStream are as follows: (1) We provide powerful, yet concise language abstractions suitable to describe conventional algorithms as streaming problems. (2) We project these abstractions onto GPUs to fully exploit their inherent massive dataparallelism. (3) We demonstrate the viability of streaming on accelerators. Experiments show that the proposed framework provides flexibility, programmability and performance gains for various benchmarks from a collection of domains, including but not limited to data streaming, data parallel problems and numerical codes.

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“…Experiments revealed that pure algorithm optimizations may lead to impressive improvements over the single CPU-core version and, even more, the improvement factor rises along with a better coordination between GPGPU and CPU [18]. Unfortunately, this is not an universal truth, mobile GPUs, for example, being designed paying much more attention to power consumption rather than focusing on performance, thus lowering the bus traffic between GPU and other devices, (e.g.…”

Section: State Of the Artmentioning

confidence: 99%

Using Graphics Processing Units for Accelerated Information Retrieval

Mocanu¹,

Din²,

Saru³

et al. 2014

SIC

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Abstract:The Parallel computing platforms enable dramatic increases in computing performance by harnessing the power of Graphics Processing Units (GPUs). Their design, based on a high level of hardware parallelization achieved through a big number of processing cores, made GPUs serious competitors for CPU based processing architectures. This fact is most obvious when it comes to processing huge amount of data. Many recent studies aimed at the development of GPU based implementations for various fields such as: astronomy, medicine, image processing, data compression and others. However, very few of them aimed at achieving information retrieval improvement based on GPU. Considering this, along with the latest stage of content based information retrieval algorithms and their practical efficiency for a wide variety of applications, this paper focuses on emphasizing parallel GPGPU algorithms performances against their CPU equivalents.

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Coordinating the use of GPU and CPU for improving performance of compute intensive applications

Cited by 70 publications

References 14 publications

Optimizing dataflow applications on heterogeneous environments

Optimizing dataflow applications on heterogeneous environments

GStream: A General-Purpose Data Streaming Framework on GPU Clusters

Using Graphics Processing Units for Accelerated Information Retrieval

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