Automatic CPU-GPU communication management and optimization

Jablin, Thomas B.; Prabhu, Prakash; Jablin, James A.; Johnson, Nick; Beard, Stephen R.; August, David I.

doi:10.1145/1993498.1993516

Cited by 104 publications

(47 citation statements)

References 15 publications

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“…CUBA allows the CPU to cache hosted data structures with a selective write-through cache policy, allowing the CPU to access hosted data structures while supporting efficient communication with the co-processors. Jablin et al [8] published a new data management model between CPU and GPU. This model manages complex and recursive datastructures without static analyses.…”

Section: Discussionmentioning

confidence: 99%

An Evaluation of Unified Memory Technology on NVIDIA GPUs

Jin

Cui

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Unified Memory is an emerging technology which is supported by CUDA 6.X. Before CUDA 6.X, the existing CUDA programming model relies on programmers to explicitly manage data between CPU and GPU and hence increases programming complexity. CUDA 6.X provides a new technology which is called as Unified Memory to provide a new programming model that defines CPU and GPU memory space as a single coherent memory (imaging as a same common address space). The system manages data access between CPU and GPU without explicit memory copy functions. This paper is to evaluate the Unified Memory technology through different applications on different GPUs to show the users how to use the Unified Memory technology of CUDA 6.X efficiently. The applications include Diffusion3D Benchmark, Parboil Benchmark Suite, and Matrix Multiplication from the CUDA SDK Samples. We changed those applications to corresponding Unified Memory versions and compare those with the original ones. We selected the NVIDIA Kepler K40 and the Jetson TK1, which can represent the latest GPUs with Kepler architecture and the first mobile platform of NVIDIA series with Kepler GPU. This paper shows that Unified Memory versions cause 10% performance loss on average. Furthermore, we used the NVIDIA Visual Profiler to dig the reason of the performance loss by the Unified Memory technology. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

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Section: Discussionmentioning

confidence: 99%

An Evaluation of Unified Memory Technology on NVIDIA GPUs

Jin

Cui

et al. 2015

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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“…We have extended the base scheduler to facilitate data reuse. In addition to the extension for data reuse, we have implemented pre-fetching and asynchronous data copy to further reduce data transfer overheads [95]. …”

Section: Methodsmentioning

confidence: 99%

Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

Kurç

Wang

et al. 2015

BMC Bioinformatics

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BackgroundWe describe a suite of tools and methods that form a core set of capabilities for researchers and clinical investigators to evaluate multiple analytical pipelines and quantify sensitivity and variability of the results while conducting large-scale studies in investigative pathology and oncology. The overarching objective of the current investigation is to address the challenges of large data sizes and high computational demands.ResultsThe proposed tools and methods take advantage of state-of-the-art parallel machines and efficient content-based image searching strategies. The content based image retrieval (CBIR) algorithms can quickly detect and retrieve image patches similar to a query patch using a hierarchical analysis approach. The analysis component based on high performance computing can carry out consensus clustering on 500,000 data points using a large shared memory system.ConclusionsOur work demonstrates efficient CBIR algorithms and high performance computing can be leveraged for efficient analysis of large microscopy images to meet the challenges of clinically salient applications in pathology. These technologies enable researchers and clinical investigators to make more effective use of the rich informational content contained within digitized microscopy specimens.

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“…Discrete GPUs are stronger than the embedded ones due to lack of area on-chip. However, a lot of performance is lost in CPU-GPU communication [36]. Embedded GPUs do not suffer from this communication overhead, yet they are not as powerful.…”

Section: P Erformancementioning

confidence: 99%

Multicore processors: Status quo and future directions

Zahran

2014

2014 10th International Computer Engineering Conference (ICENCO)

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Words like multicore, manycore, Moore's law end ing, have been around for more than a decade. How do these words describe the current status quo of computer architecture?How do they give a glimpse of the future? In this paper, we will present the status quo of the current multicore/manycore processors, and the expected future directions in light of several advances both in process technology and in system software.

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Automatic CPU-GPU communication management and optimization

Cited by 104 publications

References 15 publications

An Evaluation of Unified Memory Technology on NVIDIA GPUs

An Evaluation of Unified Memory Technology on NVIDIA GPUs

Scalable analysis of Big pathology image data cohorts using efficient methods and high-performance computing strategies

Multicore processors: Status quo and future directions

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