TH-1: China’s first petaflop supercomputer

Yang, Xuejun; Liao, Xiangke; Xu, Weiya; Song, Junqiang; Hu, Qingfeng; Su, Jinshu; Xiao, Liquan; Lü, Kai; Dou, Qi; Jiang, Juping; Yang, Chi

doi:10.1007/s11704-010-0383-x

Cited by 23 publications

(10 citation statements)

References 8 publications

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“…We also proposed a novel graph-guided logistic regression method to demonstrate the efficacy of the graphical structure. However, the experiment results on super computers (Yang et al, 2010; are expected and power efficient algorithms (Lai et al, 2015;2016) and algorithms for new infrastructures (Chen et al, 2016) are still required.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

A systematic review of structured sparse learning

Qiao

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

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High dimensional data arising from diverse scientific research fields and industrial development have led to increased interest in sparse learning due to model parsimony and computational advantage. With the assumption of sparsity, many computational problems can be handled efficiently in practice. Structured sparse learning encodes the structural information of the variables and has been quite successful in numerous research fields. With various types of structures discovered, sorts of structured regularizations have been proposed. These regularizations have greatly improved the efficacy of sparse learning algorithms through the use of specific structural information. In this article, we present a systematic review of structured sparse learning including ideas, formulations, algorithms, and applications. We present these algorithms in the unified framework of minimizing the sum of loss and penalty functions, summarize publicly accessible software implementations, and compare the computational complexity of typical optimization methods to solve structured sparse learning problems. In experiments, we present applications in unsupervised learning, for structured signal recovery and hierarchical image reconstruction, and in supervised learning in the context of a novel graph-guided logistic regression.

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Section: Conclusion and Discussionmentioning

confidence: 99%

A systematic review of structured sparse learning

Qiao

Zhang

et al. 2017

Frontiers Inf Technol Electronic Eng

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“…We herein give an emphasis on the compute and communication subsystems and refer the readers interested in TH-1A to publications [11,27] for more information. TH-1A supercomputer.…”

Section: Mapping Hlog 3 Gp To Th-1a Supercomputermentioning

confidence: 99%

“…It is built by the National University of Defense Technology and serves as the operational system of National Supercomputing Center in Tianjin. We herein give an emphasis on the compute and communication subsystems and refer the readers interested in TH-1A to publications [11,27] for more information.…”

Section: Mapping Hlog 3 Gp To Th-1a Supercomputermentioning

confidence: 99%

HLog_nGP: A parallel computation model for GPU clusters

Song

Pang

2015

Concurrency and Computation

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Parallel computation model is an abstraction for the performance characteristics of parallel computers, and should evolve with the development of computational infrastructure. The heterogeneous CPU/Graphics Processing Unit (GPU) systems have been and will be important platforms for scientific computing, which introduces an urgent demand for new parallel computation models targeting this kind of supercomputers. In this research, we propose a parallel computation model called HLog n GP to abstract the computation and communication features of heterogeneous platforms like TH-1A. All the substantial parameters of HLog n GP are in vector form and deal with the new features in GPU clusters. A simplified version HLog 3 GP of the proposed model is mapped to a specific GPU cluster and verified with two typical benchmarks. Experimental results show that HLog 3 GP outperforms the other two evaluated models and can well model the new particularities of GPU clusters. 4881 exascale systems will have much more hardware parallelism, and the development of large applications on them will be confronted with unprecedented challenges [12], including how to effectively reduce communication overhead. Therefore, the parallel computation model for large heterogeneous CPU/GPU clusters should be adept in modeling the communication operations and predicting the communication performance.In the high-performance computing scenario, communication operations can be subdivided into two categories, namely, memory communication and network communication [13]. The former indicates the transmission of data through memory hierarchy while the latter via network links. We herein give a brief survey of the prior work covering the aforementioned categories.Bosque and Pastor [14] proposed the HLogGP model to capture the heterogeneity in both compute nodes and networks. For a heterogeneous systems with M compute nodes, the five parameters of HLogGP are defined in the following forms: latency L and gap per byte G are M M matrices, while overhead o, gap between messages g and computational power P are all M -element vectors. If all the parameters are accurately determined, HLogGP can well predict the communication performance of parallel algorithms. However, HLogGP has two disadvantages: on the one hand, it is difficult to map the O.M 2 / parameters onto specific large-scale heterogeneous systems; on the other hand, it cannot model the memory communication widely witnessed in GPU platforms.With respect to memory communication, Cameron et al. have conducted in-depth study and proposed memory-logP [13] and log n P [6] models. log n P is an extension of memory-logP, and describes the effect of data continuity on communication performance. It consists of five parameters, of which n denotes the total number of communications involved in each message passing process. This model is good at modeling communication relevant to discontinuous data (e.g., Message Passing Interface (MPI) [15] derived data types), yet has routine performance for the continuous data. The r...

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“…When using a GPU for computer graphics, errors usually have minor visual impact which limits the need to handle them. When it comes to scientific computing, especially High Performance Computing, one must ensure the results are reliable and stable in time [8], [9].…”

Section: Introductionmentioning

confidence: 99%

GPUburn: A system to test and mitigate GPU hardware failures

Defour

Petit

2013

2013 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS)

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Abstract-Due to many factors such as, high transistor density, high frequency, and low voltage, today's processors are more than ever subject to hardware failures. These errors have various impacts depending on the location of the error and the type of processor. Because of the hierarchical structure of the compute units and work scheduling, the hardware failure on GPUs affect only part of the application. In this paper we present a new methodology to characterize the hardware failures of Nvidia GPUs based on a software micro-benchmarking platform implemented in OpenCL. We also present which hardware part of TESLA architecture is more sensitive to intermittent errors, which usually appears when the processor is aging. We obtained these results by accelerating the aging process by running the processors at high temperature. We show that on GPUs, intermittent errors impact is limited to a localized architecture tile. Finally, we propose a methodology to detect, record location of defective units in order to avoid them to ensure the program correctness on such architectures, improving the GPU faulttolerance capability and lifespan.

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TH-1: China’s first petaflop supercomputer

Cited by 23 publications

References 8 publications

A systematic review of structured sparse learning

A systematic review of structured sparse learning

HLog_nGP: A parallel computation model for GPU clusters

GPUburn: A system to test and mitigate GPU hardware failures

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TH-1: China’s first petaflop supercomputer

Cited by 23 publications

References 8 publications

A systematic review of structured sparse learning

A systematic review of structured sparse learning

HLognGP: A parallel computation model for GPU clusters

GPUburn: A system to test and mitigate GPU hardware failures

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HLog_nGP: A parallel computation model for GPU clusters