Embedding GPU Computations in Hadoop

Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approach...
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Autor*in:	Zhu, Jie [verfasserIn] Jiang, Hai Li, Juanjuan Hardesty, Erikson Li, Kuan-Ching Li, Zhongwen

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2014

Schlagwörter:	Hadoop MapReduce GPU CUDA

Anmerkung:	© the authors 2014

Übergeordnetes Werk:	Enthalten in: International Journal of Networked and Distributed Computing - Springer Netherlands, 2022, 2(2014), 4 vom: Okt., Seite 211-220
Übergeordnetes Werk:	volume:2 ; year:2014 ; number:4 ; month:10 ; pages:211-220

Links:	Volltext

DOI / URN:	10.2991/ijndc.2014.2.4.2

Katalog-ID:	SPR054577845

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spelling	10.2991/ijndc.2014.2.4.2 doi (DE-627)SPR054577845 (SPR)ijndc.2014.2.4.2-e DE-627 ger DE-627 rakwb eng Zhu, Jie verfasserin aut Embedding GPU Computations in Hadoop 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2014 Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. Hadoop (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Jiang, Hai aut Li, Juanjuan aut Hardesty, Erikson aut Li, Kuan-Ching aut Li, Zhongwen aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 2(2014), 4 vom: Okt., Seite 211-220 (DE-627)1006076743 2211-7946 nnns volume:2 year:2014 number:4 month:10 pages:211-220 https://dx.doi.org/10.2991/ijndc.2014.2.4.2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 10 211-220
allfields_unstemmed	10.2991/ijndc.2014.2.4.2 doi (DE-627)SPR054577845 (SPR)ijndc.2014.2.4.2-e DE-627 ger DE-627 rakwb eng Zhu, Jie verfasserin aut Embedding GPU Computations in Hadoop 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2014 Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. Hadoop (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Jiang, Hai aut Li, Juanjuan aut Hardesty, Erikson aut Li, Kuan-Ching aut Li, Zhongwen aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 2(2014), 4 vom: Okt., Seite 211-220 (DE-627)1006076743 2211-7946 nnns volume:2 year:2014 number:4 month:10 pages:211-220 https://dx.doi.org/10.2991/ijndc.2014.2.4.2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 10 211-220
allfieldsGer	10.2991/ijndc.2014.2.4.2 doi (DE-627)SPR054577845 (SPR)ijndc.2014.2.4.2-e DE-627 ger DE-627 rakwb eng Zhu, Jie verfasserin aut Embedding GPU Computations in Hadoop 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2014 Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. Hadoop (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Jiang, Hai aut Li, Juanjuan aut Hardesty, Erikson aut Li, Kuan-Ching aut Li, Zhongwen aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 2(2014), 4 vom: Okt., Seite 211-220 (DE-627)1006076743 2211-7946 nnns volume:2 year:2014 number:4 month:10 pages:211-220 https://dx.doi.org/10.2991/ijndc.2014.2.4.2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 10 211-220
allfieldsSound	10.2991/ijndc.2014.2.4.2 doi (DE-627)SPR054577845 (SPR)ijndc.2014.2.4.2-e DE-627 ger DE-627 rakwb eng Zhu, Jie verfasserin aut Embedding GPU Computations in Hadoop 2014 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © the authors 2014 Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. Hadoop (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213 Jiang, Hai aut Li, Juanjuan aut Hardesty, Erikson aut Li, Kuan-Ching aut Li, Zhongwen aut Enthalten in International Journal of Networked and Distributed Computing Springer Netherlands, 2022 2(2014), 4 vom: Okt., Seite 211-220 (DE-627)1006076743 2211-7946 nnns volume:2 year:2014 number:4 month:10 pages:211-220 https://dx.doi.org/10.2991/ijndc.2014.2.4.2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2 2014 4 10 211-220
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author	Zhu, Jie
spellingShingle	Zhu, Jie misc Hadoop misc MapReduce misc GPU misc CUDA Embedding GPU Computations in Hadoop
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topic_title	Embedding GPU Computations in Hadoop Hadoop (dpeaa)DE-He213 MapReduce (dpeaa)DE-He213 GPU (dpeaa)DE-He213 CUDA (dpeaa)DE-He213
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title	Embedding GPU Computations in Hadoop
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title_full	Embedding GPU Computations in Hadoop
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author_browse	Zhu, Jie Jiang, Hai Li, Juanjuan Hardesty, Erikson Li, Kuan-Ching Li, Zhongwen
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title_sort	embedding gpu computations in hadoop
title_auth	Embedding GPU Computations in Hadoop
abstract	Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. © the authors 2014
abstractGer	Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. © the authors 2014
abstract_unstemmed	Abstract As the size of high performance applications increases, four major challenges including heterogeneity, programmability, fault resilience, and energy efficiency have arisen in the underlying distributed systems. To tackle with all of them without sacrificing performance, traditional approaches in resource utilization, task scheduling and programming paradigm should be reconsidered. While Hadoop has handled data-intensive applications well in Clouds, GPU has demonstrated its acceleration effectiveness for computation-intensive ones. This paper addresses the approaches for Hadoop to exploiting both CPU and GPU resources effectively to handle aforementioned challenges. Hadoop schedules MapReduce’s Map and Reduce functions across multiple different computing nodes through Java, whereas CUDA code helps accelerate local computations further on attached GPUs. All available heterogeneous computational power will be utilized. MapReduce in Hadoop eases the programming task by hiding communication and scheduling details. Hadoop Distributed File System will help achieve data-level fault resilience. GPU’s energy efficiency characteristics help reduce the power consumption of the whole system. To utilize GPU in Hadoop, four approaches including Jcuda, JNI, Hadoop Streaming, and Hadoop Pipes, have been accomplished and analyzed. Experimental results have demonstrated and compared their effectiveness. © the authors 2014
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title_short	Embedding GPU Computations in Hadoop
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author2	Jiang, Hai Li, Juanjuan Hardesty, Erikson Li, Kuan-Ching Li, Zhongwen
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