CSC/ECE 506 Spring 2012/3b sk - Revision history

Jjohn: /* References */

2012-02-21T01:02:25Z

References

← Older revision		Revision as of 01:02, 21 February 2012
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	= References =		= References =
	<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>		<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce Simplified Data Processing on Large Clusters. Sanjay Ghemawat, Jeffrey Dean ] <br>
	<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>		<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors. Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>
	<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>		<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems. Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>
	<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>		<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>
	<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce] <br>		<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce] <br>
	<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>		<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems. Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>

Jjohn: /* References */

2012-02-21T00:58:10Z

References

← Older revision		Revision as of 00:58, 21 February 2012
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	= References =		= References =
	<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>		<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>
	<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>		<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>
	<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>		<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>
	<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>		<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>
	<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce ~~Tutorial~~] <br>		<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce] <br>
	<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>		<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>

Jjohn: /* References */

2012-02-21T00:57:30Z

References

← Older revision		Revision as of 00:57, 21 February 2012
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	= References =		= References =
	<span id="1foot">[[#1body\|1.]]</span> ~~MapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean~~ http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.~~pdf~~ <br>		<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>
	<span id="1foot">[[#1body\|2.]]</span> ~~Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang~~ http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf <br>		<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>
	<span id="1foot">[[#1body\|3.]]</span> Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis ~~http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf~~ <br>		<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>
	<span id="1foot">[[#1body\|4.]]</span> ~~Hadoop MapReduce~~ http://hadoop.apache.org/mapreduce/ <br>		<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>
	<span id="1foot">[[#1body\|5.]]</span> ~~Google MapReduce Tutorial~~ http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>		<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce Tutorial] <br>
	<span id="1foot">[[#1body\|6.]]</span> ~~Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis~~ http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf <br>		<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>

Jjohn: /* References */

2012-02-21T00:46:45Z

References

← Older revision		Revision as of 00:46, 21 February 2012
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	= References =		= References =
	1. http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdf <br>		<span id="1foot">[[#1body\|1.]]</span> MapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdf <br>
	2. http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf <br>		<span id="1foot">[[#1body\|2.]]</span> Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf <br>
	3. http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf <br>		<span id="1foot">[[#1body\|3.]]</span> Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf <br>
	4. http://hadoop.apache.org/mapreduce/ <br>		<span id="1foot">[[#1body\|4.]]</span> Hadoop MapReduce http://hadoop.apache.org/mapreduce/ <br>
	5. http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>		<span id="1foot">[[#1body\|5.]]</span> Google MapReduce Tutorial http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>
	6. http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf <br>		<span id="1foot">[[#1body\|6.]]</span> Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf <br>

Jjohn: /* Summary */

2012-02-21T00:30:28Z

Summary

← Older revision		Revision as of 00:30, 21 February 2012
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	Phoenix, implementation of MapReduce uses shared memory and minimizes the overheads of task spawning and data communication. With Phoenix,the programmer can provide a simple, functional expression of the algorithm and leaves parallelization and scheduling to the runtime system.Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors. Phoenix automatically handles key scheduling decisions during parallel execution. Despite runtime overheads, results have shown that performance of Phoenix to that of parallel code written in P-threads API are almost similar. Nevertheless,there are also applications that do not fit naturally in the MapReduce model for which P-threads code performs significantly better.		Phoenix, implementation of MapReduce uses shared memory and minimizes the overheads of task spawning and data communication. With Phoenix,the programmer can provide a simple, functional expression of the algorithm and leaves parallelization and scheduling to the runtime system.Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors. Phoenix automatically handles key scheduling decisions during parallel execution. Despite runtime overheads, results have shown that performance of Phoenix to that of parallel code written in P-threads API are almost similar. Nevertheless,there are also applications that do not fit naturally in the MapReduce model for which P-threads code performs significantly better.

	Graphics processors have emerged as a commodity platform for parallel computing. However, the developer requires the knowledge of the GPU architecture and much effort in developing GPU applications. Such difficulty is even more for complex and performance centric tasks such as web data analysis. Since MapReduce has been successful in easing the development of web data analysis tasks, one can use a GPU-based MapReduce for these applications. With the GPU-based framework, the developer writes their code using the simple and familiar MapReduce interfaces. The runtime on the GPU is completely hidden from the developer by ~~our~~ framework.		Graphics processors have emerged as a commodity platform for parallel computing. However, the developer requires the knowledge of the GPU architecture and much effort in developing GPU applications. Such difficulty is even more for complex and performance centric tasks such as web data analysis. Since MapReduce has been successful in easing the development of web data analysis tasks, one can use a GPU-based MapReduce for these applications. With the GPU-based framework, the developer writes their code using the simple and familiar MapReduce interfaces. The runtime on the GPU is completely hidden from the developer by the framework.

	= References =		= References =

Jjohn: /* Summary */

2012-02-21T00:29:46Z

Summary

← Older revision		Revision as of 00:29, 21 February 2012
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	Google’s MapReduce runtime implementation targets large clusters of Linux PCs connected through Ethernet switches. Tasks are forked using remote procedure calls. Buffering and communication occurs by reading and writing files on a distributed file system. The locality optimizations focus mostly on avoiding remote file accesses. While such a system is effective with distributed computing, it leads to very high overheads if used with shared-memory systems that facilitate communication through memory and are typically of much smaller scale.		Google’s MapReduce runtime implementation targets large clusters of Linux PCs connected through Ethernet switches. Tasks are forked using remote procedure calls. Buffering and communication occurs by reading and writing files on a distributed file system. The locality optimizations focus mostly on avoiding remote file accesses. While such a system is effective with distributed computing, it leads to very high overheads if used with shared-memory systems that facilitate communication through memory and are typically of much smaller scale.

	Phoenix, implementation of MapReduce ~~that~~ uses shared memory, minimizes the overheads of task spawning and data communication. With Phoenix,the programmer can provide a simple, functional expression of the algorithm and leaves parallelization and scheduling to the runtime system.Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors. Phoenix automatically handles key scheduling decisions during parallel execution. Despite runtime overheads, results have shown that performance of Phoenix to that of parallel code written in P-threads API are almost similar. Nevertheless,there are also applications that do not fit naturally in the MapReduce model for which P-threads code performs significantly better.		Phoenix, implementation of MapReduce uses shared memory and minimizes the overheads of task spawning and data communication. With Phoenix,the programmer can provide a simple, functional expression of the algorithm and leaves parallelization and scheduling to the runtime system.Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors. Phoenix automatically handles key scheduling decisions during parallel execution. Despite runtime overheads, results have shown that performance of Phoenix to that of parallel code written in P-threads API are almost similar. Nevertheless,there are also applications that do not fit naturally in the MapReduce model for which P-threads code performs significantly better.

	Graphics processors have emerged as a commodity platform for parallel computing. However, the developer requires the knowledge of the GPU architecture and much effort in developing GPU applications. Such difficulty is even more for complex and performance centric tasks such as web data analysis. Since MapReduce has been successful in easing the development of web data analysis tasks, one can use a GPU-based MapReduce for these applications. With the GPU-based framework, the developer writes their code using the simple and familiar MapReduce interfaces. The runtime on the GPU is completely hidden from the developer by our framework.		Graphics processors have emerged as a commodity platform for parallel computing. However, the developer requires the knowledge of the GPU architecture and much effort in developing GPU applications. Such difficulty is even more for complex and performance centric tasks such as web data analysis. Since MapReduce has been successful in easing the development of web data analysis tasks, one can use a GPU-based MapReduce for these applications. With the GPU-based framework, the developer writes their code using the simple and familiar MapReduce interfaces. The runtime on the GPU is completely hidden from the developer by our framework.

Jjohn: /* References */

2012-02-21T00:28:21Z

References

← Older revision		Revision as of 00:28, 21 February 2012
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	4. http://hadoop.apache.org/mapreduce/ <br>		4. http://hadoop.apache.org/mapreduce/ <br>
	5. http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>		5. http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>
			6. http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf <br>

Jjohn: /* References */

2012-02-21T00:23:00Z

References

← Older revision		Revision as of 00:23, 21 February 2012
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	3. http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf <br>		3. http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf <br>
	4. http://hadoop.apache.org/mapreduce/ <br>		4. http://hadoop.apache.org/mapreduce/ <br>
	5. http://code.google.com/edu/parallel/mapreduce-tutorial.html		5. http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>

Jjohn: /* References */

2012-02-21T00:20:14Z

References

← Older revision		Revision as of 00:20, 21 February 2012
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	= References =		= References =
	1. http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdf <br>		1. http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdf <br>
	2. http://dl.~~acm~~.~~org~~/~~citation~~.~~cfm?id=1318097~~ <br>		2. http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf <br>
	3. http://dl.~~acm~~.~~org~~/~~citation~~.~~cfm?id=1454152~~ <br>		3. http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf <br>
	4. http://hadoop.apache.org/mapreduce/ <br>		4. http://hadoop.apache.org/mapreduce/ <br>
	5. http://code.google.com/edu/parallel/mapreduce-tutorial.html		5. http://code.google.com/edu/parallel/mapreduce-tutorial.html

Jjohn: /* Summary */

2012-02-21T00:16:14Z

Summary

← Older revision		Revision as of 00:16, 21 February 2012
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	= Summary =		= Summary =
	Google’s runtime implementation targets large clusters of Linux PCs connected through Ethernet switches. Tasks are forked using remote procedure calls. Buffering and communication occurs by reading and writing files on a distributed file system. The locality optimizations focus mostly on avoiding remote file accesses. While such a system is effective with distributed computing, it leads to very high overheads if used with shared-memory systems that facilitate communication through memory and are typically of much smaller scale.		Google’s MapReduce runtime implementation targets large clusters of Linux PCs connected through Ethernet switches. Tasks are forked using remote procedure calls. Buffering and communication occurs by reading and writing files on a distributed file system. The locality optimizations focus mostly on avoiding remote file accesses. While such a system is effective with distributed computing, it leads to very high overheads if used with shared-memory systems that facilitate communication through memory and are typically of much smaller scale.

	Phoenix, implementation of MapReduce that uses shared memory, minimizes the overheads of task spawning and data communication. With Phoenix,the programmer can provide a simple, functional expression of the algorithm and leaves parallelization and scheduling to the runtime system.Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors. Phoenix automatically handles key scheduling decisions during parallel execution. Despite runtime overheads, results have shown that performance of Phoenix to that of parallel code written in P-threads API are almost similar. Nevertheless,there are also applications that do not fit naturally in the MapReduce model for which P-threads code performs significantly better.		Phoenix, implementation of MapReduce that uses shared memory, minimizes the overheads of task spawning and data communication. With Phoenix,the programmer can provide a simple, functional expression of the algorithm and leaves parallelization and scheduling to the runtime system.Phoenix leads to scalable performance for both multi-core chips and conventional symmetric multiprocessors. Phoenix automatically handles key scheduling decisions during parallel execution. Despite runtime overheads, results have shown that performance of Phoenix to that of parallel code written in P-threads API are almost similar. Nevertheless,there are also applications that do not fit naturally in the MapReduce model for which P-threads code performs significantly better.

← Older revision		Revision as of 01:02, 21 February 2012
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	= References =		= References =
	<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>		<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce Simplified Data Processing on Large Clusters. Sanjay Ghemawat, Jeffrey Dean ] <br>
	<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>		<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors. Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>
	<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>		<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems. Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>
	<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>		<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>
	<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce] <br>		<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce] <br>
	<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>		<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems. Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>

← Older revision		Revision as of 00:58, 21 February 2012
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	= References =		= References =
	<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>		<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>
	<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>		<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>
	<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>		<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>
	<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>		<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>
	<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce ~~Tutorial~~] <br>		<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce] <br>
	<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>		<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>

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	= References =		= References =
	<span id="1foot">[[#1body\|1.]]</span> ~~MapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean~~ http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.~~pdf~~ <br>		<span id="1foot">[[#1body\|1.]]</span> [http://static.googleusercontent.com/external_content/untrusted_dlcp/research.google.com/en/us/archive/mapreduce-osdi04.pdfMapReduce :Simplified Data Processing on Large Clusters Sanjay Ghemawat, Jeffrey Dean ] <br>
	<span id="1foot">[[#1body\|2.]]</span> ~~Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang~~ http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf <br>		<span id="1foot">[[#1body\|2.]]</span> [http://www.ece.rutgers.edu/~parashar/Classes/08-09/ece572/readings/mars-pact-08.pdf Mars: A MapReduce Framework on Graphics Processors Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang] <br>
	<span id="1foot">[[#1body\|3.]]</span> Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis ~~http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf~~ <br>		<span id="1foot">[[#1body\|3.]]</span> [http://csl.stanford.edu/~christos/publications/2007.cmp_mapreduce.hpca.pdf Evaluating MapReduce for Multi-core and Multiprocessor Systems Colby Ranger, Ramanan Raghuraman, Arun Penmetsa, Gary Bradski, Christos Kozyrakis] <br>
	<span id="1foot">[[#1body\|4.]]</span> ~~Hadoop MapReduce~~ http://hadoop.apache.org/mapreduce/ <br>		<span id="1foot">[[#1body\|4.]]</span> [http://hadoop.apache.org/mapreduce/ Hadoop MapReduce] <br>
	<span id="1foot">[[#1body\|5.]]</span> ~~Google MapReduce Tutorial~~ http://code.google.com/edu/parallel/mapreduce-tutorial.html <br>		<span id="1foot">[[#1body\|5.]]</span> [http://code.google.com/edu/parallel/mapreduce-tutorial.html Google MapReduce Tutorial] <br>
	<span id="1foot">[[#1body\|6.]]</span> ~~Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis~~ http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf <br>		<span id="1foot">[[#1body\|6.]]</span> [http://csl.stanford.edu/~christos/publications/2011.phoenixplus.mapreduce.pdf Phoenix++: Modular MapReduce for Shared Memory Systems Justin Talbot, Richard M.Yoo Christos Kozyrakis ] <br>