Yang You's homepage is http://www.cs.berkeley.edu/~youyang/ and his weibo is http://weibo.com/u/2082348875 Phone: 5105084506 Address: 584 Soda Hall, UC Berkeley
Breadth-First Search (BFS) is widely used in real-world applications including computational biol... more Breadth-First Search (BFS) is widely used in real-world applications including computational biology, social networks, and electronic design automation. The most effective BFS approach has been shown to be a combination of top-down and bottom-up approaches. Such hybrid techniques need to identify a switching point which is conventionally found through expensive trial-and-error and exhaustive search routines. We present an adaptive method based on regression analysis that enables dynamic switching at runtime with little overhead. We improve the performance of our method by exploiting popular heterogeneous platforms and efficiently design the approach for a given architecture. An 155x speedup is achieved over the standard top-down approach on GPUs. Our approach is the first to combine top-down and bottom-up across different architectures. Unlike combination on a single architecture, a mistuned switching point may significantly decrease the performance of cross-architecture combination. Our adap-tive method can predict the switching point with high accuracy, leading to 7× speedup compared to the switching point in average case (1,000 switching points).
2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014
ABSTRACT Support Vector Machine (SVM) has been widely used in data-mining and Big Data applicatio... more ABSTRACT Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. Advanced multi- and many-core architectures offer massive parallelism with complex memory hierarchies which can make runtime training possible, but form a barrier to efficient parallel SVM design. To address the challenges above, we designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multi-core and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools. MIC-SVM achieves 4.4-84x and 18-47x speedups against the popular LIBSVM, on MIC and Ivy Bridge CPUs respectively, for several real-world data-mining datasets. Even compared with GPUSVM, run on a top of the line NVIDIA k20x GPU, the performance of our MIC-SVM is competitive. We also conduct a cross-platform performance comparison analysis, focusing on Ivy Bridge CPUs, MIC and GPUs, and provide insights on how to select the most suitable advanced architectures for specific algorithms and input data patterns.
Breadth-First Search (BFS) is widely used in real-world applications including computational biol... more Breadth-First Search (BFS) is widely used in real-world applications including computational biology, social networks, and electronic design automation. The most effective BFS approach has been shown to be a combination of top-down and bottom-up approaches. Such hybrid techniques need to identify a switching point which is conventionally found through expensive trial-and-error and exhaustive search routines. We present an adaptive method based on regression analysis that enables dynamic switching at runtime with little overhead. We improve the performance of our method by exploiting popular heterogeneous platforms and efficiently design the approach for a given architecture. An 155x speedup is achieved over the standard top-down approach on GPUs. Our approach is the first to combine top-down and bottom-up across different architectures. Unlike combination on a single architecture, a mistuned switching point may significantly decrease the performance of cross-architecture combination. Our adap-tive method can predict the switching point with high accuracy, leading to 7× speedup compared to the switching point in average case (1,000 switching points).
2014 IEEE 28th International Parallel and Distributed Processing Symposium, 2014
ABSTRACT Support Vector Machine (SVM) has been widely used in data-mining and Big Data applicatio... more ABSTRACT Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing prediction accuracy due to insufficient runtime information, researchers can only afford to apply offline model training to avoid significant runtime training overhead. Advanced multi- and many-core architectures offer massive parallelism with complex memory hierarchies which can make runtime training possible, but form a barrier to efficient parallel SVM design. To address the challenges above, we designed and implemented MIC-SVM, a highly efficient parallel SVM for x86 based multi-core and many-core architectures, such as the Intel Ivy Bridge CPUs and Intel Xeon Phi co-processor (MIC). We propose various novel analysis methods and optimization techniques to fully utilize the multilevel parallelism provided by these architectures and serve as general optimization methods for other machine learning tools. MIC-SVM achieves 4.4-84x and 18-47x speedups against the popular LIBSVM, on MIC and Ivy Bridge CPUs respectively, for several real-world data-mining datasets. Even compared with GPUSVM, run on a top of the line NVIDIA k20x GPU, the performance of our MIC-SVM is competitive. We also conduct a cross-platform performance comparison analysis, focusing on Ivy Bridge CPUs, MIC and GPUs, and provide insights on how to select the most suitable advanced architectures for specific algorithms and input data patterns.
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