Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most wellknown density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.
基于密度的聚类算法是一种广泛使用的数据挖掘技术，它可以发现不规则形状的聚类和聚类数据，而无需事先知道它包含的聚类数量。DBSCAN是最著名的基于密度的聚类算法。我们介绍我们的DBSCAN版本，称为Mr. Scan，它使用混合并行实现，将基于MRNet树的分布网络与配备gpgpu的节点相结合。Mr. Scan通过有效地划分点空间和优化DBSCAN在密集数据区域上的计算，避免了现有实现的问题。我们在Twitter的地理定位数据集和斯隆数字巡天(Sloan Digital Sky Survey)获得的图像数据上对Scan先生进行了测试。在最大规模的情况下，Scan在17.3分钟内在Cray Titan的8,192个GPU节点上从Twitter数据集中聚集了65亿个点。所有其他并行DBSCAN实现都只展示了最多可集群1亿个点的能力。
{"title":"Mr. Scan: Extreme scale density-based clustering using a tree-based network of GPGPU nodes","authors":"Benjamin Welton, Evan Samanas, B. Miller","doi":"10.1145/2503210.2503262","DOIUrl":"https://doi.org/10.1145/2503210.2503262","url":null,"abstract":"Density-based clustering algorithms are a widely-used class of data mining techniques that can find irregularly shaped clusters and cluster data without prior knowledge of the number of clusters it contains. DBSCAN is the most wellknown density-based clustering algorithm. We introduce our version of DBSCAN, called Mr. Scan, which uses a hybrid parallel implementation that combines the MRNet tree-based distribution network with GPGPU-equipped nodes. Mr. Scan avoids the problems of existing implementations by effectively partitioning the point space and by optimizing DBSCAN's computation over dense data regions. We tested Mr. Scan on both a geolocated Twitter dataset and image data obtained from the Sloan Digital Sky Survey. At its largest scale, Mr. Scan clustered 6.5 billion points from the Twitter dataset on 8,192 GPU nodes on Cray Titan in 17.3 minutes. All other parallel DBSCAN implementations have only demonstrated the ability to cluster up to 100 million points.","PeriodicalId":371074,"journal":{"name":"2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124149674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Intel has recently introduced Intel® Transactional Synchronization Extensions (Intel® TSX) in the Intel 4th Generation Core™ Processors. With Intel TSX, a processor can dynamically determine whether threads need to serialize through lock-protected critical sections. In this paper, we evaluate the first hardware implementation of Intel TSX using a set of high-performance computing (HPC) workloads, and demonstrate that applying Intel TSX to these workloads can provide significant performance improvements. On a set of real-world HPC workloads, applying Intel TSX provides an average speedup of 1.41x. When applied to a parallel user-level TCP/IP stack, Intel TSX provides 1.31x average bandwidth improvement on network intensive applications. We also demonstrate the ease with which we were able to apply Intel TSX to the various workloads.
{"title":"Performance evaluation of Intel® Transactional Synchronization Extensions for high-performance computing","authors":"Richard M. Yoo, C. Hughes, K. Lai, Ravi Rajwar","doi":"10.1145/2503210.2503232","DOIUrl":"https://doi.org/10.1145/2503210.2503232","url":null,"abstract":"Intel has recently introduced Intel® Transactional Synchronization Extensions (Intel® TSX) in the Intel 4th Generation Core™ Processors. With Intel TSX, a processor can dynamically determine whether threads need to serialize through lock-protected critical sections. In this paper, we evaluate the first hardware implementation of Intel TSX using a set of high-performance computing (HPC) workloads, and demonstrate that applying Intel TSX to these workloads can provide significant performance improvements. On a set of real-world HPC workloads, applying Intel TSX provides an average speedup of 1.41x. When applied to a parallel user-level TCP/IP stack, Intel TSX provides 1.31x average bandwidth improvement on network intensive applications. We also demonstrate the ease with which we were able to apply Intel TSX to the various workloads.","PeriodicalId":371074,"journal":{"name":"2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129077091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cindy Rubio-González, Cuong Nguyen, Hong Diep Nguyen, J. Demmel, W. Kahan, Koushik Sen, D. Bailey, Costin Iancu, David G. Hough
Given the variety of numerical errors that can occur, floating-point programs are difficult to write, test and debug. One common practice employed by developers without an advanced background in numerical analysis is using the highest available precision. While more robust, this can degrade program performance significantly. In this paper we present Precimonious, a dynamic program analysis tool to assist developers in tuning the precision of floating-point programs. Precimonious performs a search on the types of the floating-point program variables trying to lower their precision subject to accuracy constraints and performance goals. Our tool recommends a type instantiation that uses lower precision while producing an accurate enough answer without causing exceptions. We evaluate Precimonious on several widely used functions from the GNU Scientific Library, two NAS Parallel Benchmarks, and three other numerical programs. For most of the programs analyzed, Precimonious reduces precision, which results in performance improvements as high as 41%.
{"title":"Precimonious: Tuning assistant for floating-point precision","authors":"Cindy Rubio-González, Cuong Nguyen, Hong Diep Nguyen, J. Demmel, W. Kahan, Koushik Sen, D. Bailey, Costin Iancu, David G. Hough","doi":"10.1145/2503210.2503296","DOIUrl":"https://doi.org/10.1145/2503210.2503296","url":null,"abstract":"Given the variety of numerical errors that can occur, floating-point programs are difficult to write, test and debug. One common practice employed by developers without an advanced background in numerical analysis is using the highest available precision. While more robust, this can degrade program performance significantly. In this paper we present Precimonious, a dynamic program analysis tool to assist developers in tuning the precision of floating-point programs. Precimonious performs a search on the types of the floating-point program variables trying to lower their precision subject to accuracy constraints and performance goals. Our tool recommends a type instantiation that uses lower precision while producing an accurate enough answer without causing exceptions. We evaluate Precimonious on several widely used functions from the GNU Scientific Library, two NAS Parallel Benchmarks, and three other numerical programs. For most of the programs analyzed, Precimonious reduces precision, which results in performance improvements as high as 41%.","PeriodicalId":371074,"journal":{"name":"2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)","volume":"196 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116674858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (218) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (40963) particle cosmological simulations, accounting for 4 × 1020 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.
{"title":"2HOT: An improved parallel hashed oct-tree N-Body algorithm for cosmological simulation","authors":"Michael S. Warren","doi":"10.1145/2503210.2503220","DOIUrl":"https://doi.org/10.1145/2503210.2503220","url":null,"abstract":"We report on improvements made over the past two decades to our adaptive treecode N-body method (HOT). A mathematical and computational approach to the cosmological N-body problem is described, with performance and scalability measured up to 256k (218) processors. We present error analysis and scientific application results from a series of more than ten 69 billion (40963) particle cosmological simulations, accounting for 4 × 1020 floating point operations. These results include the first simulations using the new constraints on the standard model of cosmology from the Planck satellite. Our simulations set a new standard for accuracy and scientific throughput, while meeting or exceeding the computational efficiency of the latest generation of hybrid TreePM N-body methods.","PeriodicalId":371074,"journal":{"name":"2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124746603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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