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Data parallel Large Sparse Deep Neural Network on GPU 34

Data Parallel Large Sparse Deep Neural Network on GPU

引用

34th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Sattar, Naw Safrin Arifuzzaman, Shaikh Univ New Orleans Dept Comp Sci New Orleans LA 70148 USA

ISBN: (纸本)9781728174457

Sparse Deep Neural Network (DNN) is an emerging research area since deploying deep neural networks with limited resources is very challenging. In this work, we provide a scalable solution to the Sparse DNN Challenge-a challenge posed by MIT/IEEE/Amazon ***-by designing data parallelism on GPUs. We provide a solution based on Python TensorFlow as it is a widely used tool in different scientific applications for deep learning. We use the datasets provided by GraphChallenge, derived from the MNIST handwritten letters. We use the Synthetic DNNs from RadiX-Net with varying number of neurons and layers. We implement a data parallel implementation of Sparse DNN using TensorFlow on GPU. Our solution shows up to 4.7x speedup over the basehne serial MATLAB implementation given in GraphChallenge. In addition to that, our TensorFlow GPU implementation demonstrates a 3-fold speedup over our TensorFloW CPU implementation.

关键词： deep neural network sparse data parallel computing GPU TensorFlow

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MapReduce Across distributed Clusters for Data-intensive applications

MapReduce Across Distributed Clusters for Data-intensive App...

引用

26th IEEE international parallel and distributed processing symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Wang, Lizhe Tao, Jie Marten, Holger Streit, Achim Khan, Samee U. Kolodziej, Joanna Chen, Dan Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China Steinbuch Ctr Comp Karlsruhe Inst Technol Karlsruhe Germany North Dakota State Univ Dept Elect & Comp Engn Fargo ND 58105 USA Univ Bielsko Biala Dept Math & Comp Sci Bielsko Biala Poland China Univ Geosci Sch Comp Beijing Peoples R China

ISBN: (纸本)9780769546766

Recently, the computational requirements for largescale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. this huge amount of data are processed on more than 140 computing centers distributed across 34 countries. the MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of G-Hadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks across distributed clusters. Experiments of the G-Hadoop framework on distributed clusters show encouraging results.

关键词： MapReduce Hadoop Data Intensive Computing

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An On-the-fly Scheduling Strategy for distributed Stream processing Platform 16

An On-the-fly Scheduling Strategy for Distributed Stream Pro...

引用

16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Wang, Wen'an Zhang, Chuang Chen, Xiaojun Li, Zhao Ding, Hong Wen, Xin Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9781728111414

distributed stream processing can accomplish real-time processing of continuous streaming big data to obtain valuable information with high velocity. To maintain continuously stable and efficient running of stream applications, however, continuous online scheduling operations are required in the context of highly dynamic data stream. For this reason, this paper proposes the on-the-fly scheduling strategy in a distributed stream processing environment, which dynamically predicts abnormal events through double exponential smoothing and adopts trafficaware active migration protocol to adjust the network routing structure on-the-fly to balance the inter-worker load. Moreover, an evaluation method is proposed to quantitatively analyze the various scheduling objectives. Finally, we commendably apply the scheduling strategy to a stream processing platform, which regards docker instance as basic scheduling units. Meanwhile, based on the platform and the evaluation method, we complete performance comparison experiments of the scheduling algorithm. the experimental results indicate that our algorithm has excellent performance in throughput of topology, average processing time and balance of task load, which is suitable for deployment in a distributed environment with large-scale nodes and tasks.

关键词： distributed stream processing prediction algorithm exponential smoothing traffic-aware migration evaluation method

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CALCioM: Mitigating I/O Interference in HPC Systems through Cross-Application Coordination

CALCioM: Mitigating I/O Interference in HPC Systems through ...

引用

IEEE 28th international parallel & distributed processing symposium (IPDPS)

作者： Dorier, Maahieu Antoniu, Gabriel Ross, Rob Kimpe, Dries Ibrahim, Shadi ENS Cachan Brittany IRISA Rennes France INRIA Rennes Bretagne Atlant Rennes France Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9780769552071

Unmatched computation and storage performance in new HPC systems have led to a plethora of I/O optimizations ranging from application-side collective I/O to network and disk-level request scheduling on the file system side. As we deal with ever larger machines, the interference produced by multiple applications accessing a shared parallel file system in a concurrent manner becomes a major problem. Interference often breaks single-application I/O optimizations, dramatically degrading application I/O performance and, as a result, lowering machine wide efficiency. this paper focuses on CALCioM, a framework that aims to mitigate I/O interference through the dynamic selection of appropriate scheduling policies. CALCioM allows several applications running on a supercomputer to communicate and coordinate their I/O strategy in order to avoid interfering with one another. In this work, we examine four I/O strategies that can be accommodated in this framework: serializing, interrupting, interfering and coordinating. Experiments on Argonne's BG/P Surveyor machine and on several clusters of the French Grid' 5000 show how CALCioM can be used to efficiently and transparently improve the scheduling strategy between two otherwise interfering applications, given specified metrics of machine wide efficiency.

关键词： Exascale I/O parallel File Systems Cross-Application Contention Interference CALCioM

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Optimizations and Analysis of BSP Graph processing Models on Public Clouds

Optimizations and Analysis of BSP Graph Processing Models on...

引用

IEEE 27th international parallel and distributed processing symposium (IPDPS)

作者： Redekopp, Mark Simmhan, Yogesh Prasanna, Viktor K. Univ So Calif Los Angeles CA 90089 USA

ISBN: (纸本)9780769549712

Large-scale graph analytics is a central tool in many fields, and exemplifies the size and complexity of Big Data applications. Recent distributed graph processing frameworks utilize the venerable Bulk Synchronous parallel (BSP) model and promise scalability for large graph analytics. this has been made popular by Google's Pregel, which provides an architecture design for BSP graph processing. Public clouds offer democratized access to medium-sized compute infrastructure with the promise of rapid provisioning with no capital investment. Evaluating BSP graph frameworks on cloud platforms with their unique constraints is less explored. Here, we present optimizations and analyses for computationally complex graph analysis algorithms such as betweenness-centrality and all-pairs shortest paths on a native BSP framework we have developed for the Microsoft Azure Cloud, modeled on the Pregel graph processing model. We propose novel heuristics for scheduling graph vertex processing in swaths to maximize resource utilization on cloud VMs that lead to a 3.5x performance improvement. We explore the effects of graph partitioning in the context of BSP, and show that even a well partitioned graph may not lead to performance improvements due to BSP's barrier synchronization. We end with a discussion on leveraging cloud elasticity for dynamically scaling the number of BSP workers to achieve a better performance than a static deployment, and at a significantly lower cost.

关键词： Graph analytics Cloud computing Pregel MapReduce Bulk Synchronous parallel Betweennness centrality

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Demystifying the Performance of HPC Scientific applications on NVM-based Memory Systems 34

Demystifying the Performance of HPC Scientific Applications ...

引用

34th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Peng, Ivy Wu, Kai Ren, Jie Li, Dong Gokhale, Maya Lawrence Livermore Natl Lab Livermore CA 94550 USA Univ Calif Merced Merced CA USA

ISBN: (纸本)9781728168760

the emergence of high-density byte-addressable non-volatile memory (NVM) is promising to accelerate data- and compute-intensive applications. Current NVM technologies have lower performance than DRAM and, thus, are often paired with DRAM in a heterogeneous main memory. Recently, byte-addressable NVM hardware becomes available. this work provides a timely evaluation of representative HPC applications from the "Seven Dwarfs" on NVM-based main memory. Our results quantify the effectiveness of DRAM-cached-NVM for accelerating HPC applications and enabling large problems beyond the DRAM capacity. On uncached-NVM, HPC applications exhibit three tiers of performance sensitivity, i.e., insensitive, scaled, and bottlenecked. We identify write throttling and concurrency control as the priorities in optimizing applications. We highlight that concurrency change may have a diverging effect on read and write accesses in applications. Based on these findings, we explore two optimization approaches. First, we provide a prediction model that uses datasets from a small set of configurations to estimate performance at various concurrency and data sizes to avoid exhaustive search in the configuration space. Second, we demonstrate that write-aware data placement on uncached-NVM could achieve 2x performance improvement with a 60% reduction in DRAM usage.

关键词： Non-volatile memory Optane heterogeneous memory persistent memory byte-addressable NVM HPC

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On the Correctness of Mixing Lazy and Eager Version Management in Transactions

On the Correctness of Mixing Lazy and Eager Version Manageme...

引用

26th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Zhao, Lihang Draper, Jeff Univ So Calif Inst Informat Sci Marina Del Rey CA 90292 USA

ISBN: (纸本)9780769546766

Transactional memory has been proposed as an optimistic concurrency-control construct to ease parallel programming. Hardware transactional memory (HTM) approaches implement version management and conflict detection in hardware to guarantee the correctness of transaction execution. Based on the style of version management and conflict detection, state-of-the-art HTM systems fall into two main types, namely lazy systems and eager systems. Neither system type is able to always perform better than the other over a wide range of applications due to the broad variations in the execution behaviors in typical parallel workloads. In this paper, we focus on the correctness of mixing lazy and eager version management in a log-based HTM. this hybrid type of version management is demonstrated to satisfy the requirement of atomicity and conflict serializability, which are critical for correctness. Furthermore, it is shown that the new approach does not violate the memory coherence formal model.

关键词： parallel programming

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Smart Streaming: A High-throughput Fault-tolerant Online processing System 34

Smart Streaming: A High-Throughput Fault-tolerant Online Pro...

引用

34th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Guo, Jia Agrawal, Gagan Ohio State Univ Comp Sci & Engn Columbus OH 43210 USA Augusta Univ Sch Comp & Cybersci Augusta GA 30912 USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174457

In recent years, there has been considerable interest in developing frameworks for processing streaming data. Like the precursor commercial systems for data-intensive processing, these systems have largely not used methods popular within the HPC community (for example, MPI for communication). In this paper, we demonstrate a system for stream processing that offers a high-level API to the users (similar to MapReduce), is fault-tolerant, and is also more efficient and scalable than current solutions. Particularly, a cost-efficient MPI/OpenMP based fault-tolerant scheme is incorporated so that the system can survive node failures with only a modest degradation of performance. We evaluate both the functionality and efficiency of Smart Streaming using four common applications in machine learning and data analytics. A comparison against state-of-the-art streaming frameworks shows our system boosts the throughput of test cases by up to 10X and achieve desirable parallelism when scaled out. Additionally, the performance loss upon failures is only proportional to the share of failed resources.

关键词： Fault tolerance Fault tolerant systems Sparks throughput Data analysis Real-time systems Machine learning

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GraphPad: Optimized Graph Primitives for parallel and distributed Platforms 30

GraphPad: Optimized Graph Primitives for Parallel and Distri...

引用

30th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Anderson, Michael J. Sundaram, Narayanan Satish, Nadathur Patwary, Md Mostofa Ali Willke, theodore L. Dubey, Pradeep Intel Corp Parallel Comp Lab Santa Clara CA 95052 USA

ISBN: (纸本)9781509021406

the duality between graphs and matrices means that many common graph analyses can be expressed with primitives such as generalized sparse matrix-vector multiplication (SpMSpV) and sparse matrix-matrix multiplication (SpGEMM). Achieving high performance on these primitives is challenging due to limited arithmetic intensity, irregular memory accesses, and significant network communication requirements in the distributed setting. In this paper we implement four graph applications using GraphPad, our optimized multinode implementations of generalized linear algebra primitives such as SpMSpV and SpGEMM. GraphPad is highly flexible to accommodate multiple data layouts, partitioning strategies, and incorporates communication optimizations. Our performance at scale can exceed that of CombBLAS by up to 40x. In addition to GraphPad's performance in a distributed setting, it is also within 2x the performance of GraphMat, a high performance graph framework on a single node for four out of five benchmarks. We also show our communication optimizations and flexibility are critical for good performance on both HPC clusters and commodity cloud platforms.

关键词： Graph analytics SpGEMM SpMSpV

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Scalability Challenges for Massively parallel AMR applications

Scalability Challenges for Massively Parallel AMR Applicatio...

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23rd IEEE international parallel and distributed processing symposium

作者： Van Straalen, Brian Shalf, John Ligocki, Terry Keen, Noel Yang, Woo-Sun Univ Calif Berkeley Lawrence Berkeley Lab ANAG Berkeley CA 94720 USA Univ Calif Berkeley Lawrence Berkeley Natl Lab NERSC Berkeley CA 94720 USA

ISBN: (纸本)9781424437511

PDE solvers using Adaptive Mesh Refinement on block structured grids are some of the most challenging applications to adapt to massively parallel computing environments. We describe optimizations to the Chombo AMR framework that enable it to scale efficiently to thousands of processors on the Cray XT4. the optimization process also uncovered OS-related performance variations that were not explained by conventional OS interference benchmarks. Ultimately the variability was traced back to complex interactions between the application, system software, and the memory hierarchy. Once identified, software modifications to control the variability improved performance by 20% and decreased the variation in computation time across processors by a factor of 3. these newly identified sources of variation will impact many applications and suggest new benchmarks for OS-services be developed.

关键词： Application programs

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