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共 261 条记录，以下是61-70 订阅

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Communication-free Massively distributed Graph Generation 32

Communication-free Massively Distributed Graph Generation

引用

27th international Heterogeneity in Computing Workshop in conjunction with 32nd ieee international parallel and distributed processing symposium (ipdps)

作者： Funke, Daniel Lamm, Sebastian Sanders, Peter Schulz, Christian Strash, Darren von Looz, Moritz Karlsruhe Inst Technol Karlsruhe Germany Univ Vienna Vienna Austria Colgate Univ New York NY USA

ISBN: (纸本)9781538643686

Analyzing massive complex networks yields promising insights about our everyday lives. Building scalable algorithms to do so is a challenging task that requires a careful analysis and an extensive evaluation. However, engineering such algorithms is often hindered by the scarcity of publicly available datasets. Network generators serve as a tool to alleviate this problem by providing synthetic instances with controllable parameters. However, many network generators fail to provide instances on a massive scale due to their sequential nature or resource constraints. Additionally, truly scalable network generators are few and often limited in their realism. In this work, we present novel generators for a variety of network models commonly found in practice. By making use of pseudorandomization and divide-and-conquer schemes, our generators follow a communication-free paradigm. the resulting generators are often embarrassingly parallel and have a near optimal scaling behavior. this allows us to generate instances of up to 2 43 vertices and 2 47 edges in less than 22 minutes on 32768 cores. therefore, our generators allow new graph families to be used on an unprecedented

关键词： graph generation communication-free distributed algorithms

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Efficient Gradient Boosted Decision Tree Training on GPUs 32

Efficient Gradient Boosted Decision Tree Training on GPUs

引用

27th international Heterogeneity in Computing Workshop in conjunction with 32nd ieee international parallel and distributed processing symposium (ipdps)

作者： Wen, Zeyi He, Bingsheng Ramamohanarao, Kotagiri Lu, Shengliang Shi, Jiashuai Natl Univ Singapore Singapore Singapore Univ Melbourne Melbourne Vic Australia South China Univ Technol Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538643686

In this paper, we present a novel parallel implementation for training Gradient Boosting Decision Trees (GBDTs) on Graphics processing Units (GPUs). thanks to the wide use of the open sourced XGBoost library, GBDTs have become very popular in recent years and won many awards in machine learning and data mining competitions. Although GPUs have demonstrated their success in accelerating many machine learning applications, there are a series of key challenges of developing a GPU-based GBDT algorithm, including irregular memory accesses, many small sorting operations and varying data parallel granularities in tree construction. To tackle these challenges on GPUs, we propose various novel techniques (including Run-length Encoding compression and thread/ block workload dynamic allocation, and reusing intermediate training results for efficient gradient computation). Our experimental results show that our algorithm named GPU-GBDT is often 10 to 20 times faster than the sequential version of XGBoost, and achieves 1.5 to 2 times speedup over a 40 threaded XGBoost running on a relatively high-end workstation of 20 CPU cores. Moreover, GPU-GBDT outperforms its CPU counterpart by 2 to 3 times in terms of performance-price ratio.

关键词： Training Graphics processing units parallel processing Decision trees Instruction sets Machine learning algorithms Machine learning"

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Blocking Optimization Techniques for Sparse Tensor Computation 32

Blocking Optimization Techniques for Sparse Tensor Computati...

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27th international Heterogeneity in Computing Workshop in conjunction with 32nd ieee international parallel and distributed processing symposium (ipdps)

作者： Choi, Jee W. Liu, Xing Smith, Shaden Simon, Tyler IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Intel Corp Parallel Comp Lab Santa Clara CA USA Univ Minnesota Minneapolis MN USA Univ Maryland Baltimore MD 21201 USA

ISBN: (纸本)9781538643686

We present a detailed analysis of the sparse matricized tensor times Khatri-Rao product (MTTKRP) kernel that is the key bottleneck in various sparse tensor computations. By using the well-known roofline model and carefully instrumenting the state-of-the-art MTTKRP code with the pressure point analysis technique, we show that the performance of MTTKRP is highly sensitive to data traffic like other sparse computations. We also identify key performance bottlenecks that diverge from our prior knowledge of sparse MTTKRP on modern processors. We propose to use blocking optimization techniques to address the bottlenecks identified within the MTTKRP computation. By using a combination of two blocking techniques, we achieve more than 3.5x and 2.0x speedup over the state-of-the-art MTTKRP implementation in the SPLATT library on four real-world data sets and two synthetically generated data sets, respectively. We also employ a new data partitioning technique for the distributed MTTKRP implementation, which provides an additional dimension of scalability. As a result, our implementation exhibits good strong scaling and a 1.6x speedup on 64 nodes for two real-world data sets, when compared to the SPLATT library.

关键词： tensor decomposition canonical polyadic MPI distributed high-performance computing MTTKRP

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Efficient and Scalable Retrieval Techniques for Global File Properties

Efficient and Scalable Retrieval Techniques for Global File ...

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ieee 27th international parallel and distributed processing symposium (ipdps)

作者： Ahn, Dong H. Brim, Michael J. de Supinski, Bronis R. Gamblin, Todd Lee, Gregory L. LeGendre, Matthew P. Miller, Barton P. Moody, Adam Schulz, Martin Lawrence Livermore Natl Lab Computat Directorate Livermore CA 94550 USA Univ Wisconsin Comp Sci Dept Madison WI 53706 USA

ISBN: (纸本)9780769549712

Large-scale systems typically mount many different file systems with distinct performance characteristics and capacity. Applications must efficiently use this storage in order to realize their full performance potential. Users must take into account potential file replication throughout the storage hierarchy as well as contention in lower levels of the I/O system, and must consider communicating the results of file I/O between application processes to reduce file system accesses. Addressing these issues and optimizing file accesses requires detailed run-time knowledge of file system performance characteristics and the location(s) of files on them. In this paper, we propose Fast Global File Status (FGFS), a scalable mechanism to retrieve file information, such as its degree of distribution or replication and consistency. We use a novel node-local technique that turns expensive, non-scalable file system calls into simple string comparison operations. FGFS raises the namespace of a locally-defined file path to a global namespace with little or no file system calls to obtain global file properties efficiently. Our evaluation on a large multi-physics application shows that most FGFS file status queries on its executable and 848 shared library files complete in 272 milliseconds or faster at 32,768 MPI processes. Even the most expensive operation, which checks global file consistency, completes in under 7 seconds at this scale, an improvement of several orders of magnitude over the traditional checksum technique.

关键词： application program interfaces file organisation large-scale systems message passing query processing shared memory systems

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High-Productivity and High-Performance Analysis of Filtered Semantic Graphs

High-Productivity and High-Performance Analysis of Filtered ...

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ieee 27th international parallel and distributed processing symposium (ipdps)

作者： Buluc, Aydin Duriakova, Erika Fox, Armando Gilbert, John R. Kamil, Shoaib Lugowski, Adam Oliker, Leonid Williams, Samuel Univ Calif Berkeley Lawrence Berkeley Natl Lab CRD Berkeley CA 94720 USA Univ Dublin Coll Sch Comp Sci & Informat Dublin 2 Ireland Univ Calif Santa Barbara Dept Comp Sci Santa Barbara CA 93106 USA Univ Calif Berkeley EECS Dept Berkeley CA 94720 USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9780769549712

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry attributes of various types. Analytic queries on semantic graphs typically depend on the values of these attributes;thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, is customizable in two ways. First, the user can write custom graph algorithms by specifying operations between edges and vertices. these programmer-specified operations are called semiring operations due to KDT's underlying linear-algebraic abstractions. Second, the user can customize existing graph algorithms by writing filters that return true for those vertices and edges the user wants to retain during algorithm execution. For high productivity, both semiring operations and filters are written in a high-level language, resulting in relatively low performance due to the bottleneck of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine, and show our approach enables users to write in high-level languages and still obtain the high performance of low-level code. We also present a new roofline model for graph traversals, and show that our high-performance implementations do not significantly deviate from the roofline. Overall, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs.

关键词： data mining graph theory high level languages information filtering operating systems (computers) parallel processing virtual machines

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Resource Management in VMware Powered Cloud: Concepts and Techniques

Resource Management in VMware Powered Cloud: Concepts and Te...

引用

ieee 27th international parallel and distributed processing symposium (ipdps)

作者： Padala, Pradeep VMware Distributed Resource Management Team Member

ISBN: (纸本)9780769549712

In this tutorial we will discuss three layers of VMware software stack that work together to create a cloud environment. these layers include ESX hypervisor, VirtualCenter (vCenter) for management and vCloud Director to combine various virtual centers in to a scalable cloud environment. I will describe each one of these in more detail, while focusing on resource management controls that are provided at each layer. First, I will present the high level architecture of ESX and resource management model that is available for better performance isolation among VMs and high consolidation. ESX provides a very rich set of controls for all resources including CPU, memory, network and storage IO. the next step in resource management is providing techniques to handle a group or cluster of hosts. VMware technologies such as DRS can take care of automatically placing VMs on a cluster and performing load balancing across hosts using live migration of VMs (a.k.a. vMotion). Similarly a recent feature called Storage DRS provides initial placement of virtual disks on a cluster of storage devices and does load balancing across storage devices using live virtual disk migration. I will conclude with our cloud product called vCloud Director, which allows federation of multiple vCenters.

关键词： cloud computing resource allocation virtual machines

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Euler Meets GPU: Practical Graph Algorithms with theoretical Guarantees 35

Euler Meets GPU: Practical Graph Algorithms with Theoretical...

引用

35th ieee international parallel and distributed processing symposium (ipdps)

作者： Polak, Adam Siwiec, Adrian Stobierski, Michal Jagiellonian Univ Fac Math & Comp Sci Krakow Poland

ISBN: (纸本)9781665440660

the Euler tour technique is a classical tool for designing parallel graph algorithms, originally proposed for the PRAM model. We ask whether it can be adapted to run efficiently on GPU. We focus on two established applications of the technique: (1) the problem of finding lowest common ancestors (LCA) of pairs of nodes in trees, and (2) the problem of finding bridgis in undirected graphs. In our experiments, we compare theoretically optimal algorithms using the Euler tour technique against simpler heuristics supposed to perform particularly well on typical instances. We show that the Euler tour-based algorithms not only fulfill their theoretical promises and outperform practical heuristics on hard instances, but also perform on par with them on easy instances.

关键词： graph algorithms parallel algorithms

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parallel scheduling of DAGs under memory constraints 32

Parallel scheduling of DAGs under memory constraints

引用

27th international Heterogeneity in Computing Workshop in conjunction with 32nd ieee international parallel and distributed processing symposium (ipdps)

作者： Marchal, Loris Nagy, Hanna Simon, Bertrand Vivien, Frederic ENS Lyon INRIA CNRS 46 Allee Italie Lyon France Univ Lyon LIP ENS Lyon 46 Allee Italie Lyon France Tech Univ Cluj Napoca Str Memorandumului 28 Cluj Napoca 400114 Romania

ISBN: (纸本)9781538643686

Scientific workflows are frequently modeled as Directed Acyclic Graphs (DAG) of tasks, which represent computational modules and their dependencies in the form of data produced by a task and used by another one. this formulation allows the use of runtime systems which dynamically allocate tasks onto the resources of increasingly complex computing platforms. However, for some workflows, such a dynamic schedule may run out of memory by exposing too much parallelism. this paper focuses on the problem of transforming such a DAG to prevent memory shortage, and concentrates on shared memory platforms. We first propose a simple model of DAGs which is expressive enough to emulate complex memory behaviors. We then exhibit a polynomial-time algorithm that computes the maximum peak memory of a DAG, that is, the maximum memory needed by any parallel schedule. We consider the problem of reducing this maximum peak memory to make it smaller than a given bound by adding new fictitious edges, while trying to minimize the critical path of the graph. After proving this problem NP-complete, we provide an ILP solution as well as several heuristic strategies that are thoroughly compared by simulation on synthetic DAGs modeling actual computational workflows. We show that on most instances we are able to decrease the maximum peak memory at the cost of a small increase in the critical path, thus with little impact on quality of the final parallel schedule.

关键词： Task analysis Schedules Memory management Computational modeling Dynamic scheduling Runtime Data models"

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Highly Efficient Compensation-based parallelism for Wavefront Loops on GPUs 32

Highly Efficient Compensation-based Parallelism for Wavefron...

引用

27th international Heterogeneity in Computing Workshop in conjunction with 32nd ieee international parallel and distributed processing symposium (ipdps)

作者： Hou, Kaixi Wang, Hao Feng, Wu-chun Vetter, Jeffrey S. Lee, Seyong Virginia Tech Dept Comp Sci Blacksburg VA 24061 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781538643686

Wavefront loops are widely used in many scientific applications, e.g., partial differential equation (PDE) solvers and sequence alignment tools. However, due to the data dependencies in wavefront loops, it is challenging to fully utilize the abundant compute units of GPUs and to reuse data through their memory hierarchy. Existing solutions can only optimize for these factors to a limited extent. For example, tiling-based methods optimize memory access but may result in load imbalance;while compensation-based methods, which change the original order of computation to expose more parallelism and then compensate for it, suffer from both global synchronization overhead and limited generality. In this paper, we first prove under which circumstances that breaking data dependencies and properly changing the sequence of computation operators in our compensation-based method does not affect the correctness of results. Based on this analysis, we design a highly efficient compensation-based parallelism on GPUs. Our method provides weighted scan-based GPU kernels to optimize the computation and combines with the tiling method to optimize memory access and synchronization. the performance results on the NVIDIA K80 and P100 GPU platforms demonstrate that our method can achieve significant improvements for four types of real-world application kernels over the state-of-the-art research.

关键词： parallel processing Synchronization Kernel Graphics processing units Layout Partial differential equations Tools"

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Implicit Decomposition for Write-Efficient Connectivity Algorithms 32

Implicit Decomposition for Write-Efficient Connectivity Algo...

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27th international Heterogeneity in Computing Workshop in conjunction with 32nd ieee international parallel and distributed processing symposium (ipdps)

作者： Ben-David, Naama Blelloch, Guy E. Fineman, Jeremy T. Gibbons, Phillip B. Gu, Yan McGuffey, Charles Shun, Julian Carnegie Mellon Univ Pittsburgh PA 15213 USA Georgetown Univ Washington DC 20057 USA MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781538643686

the future of main memory appears to lie in the direction of new technologies that provide strong capacity-toperformance ratios, but have write operations that are much more expensive than reads in terms of latency, bandwidth, and energy. Motivated by this trend, we propose sequential and parallel algorithms to solve graph connectivity problems using significantly fewer writes than conventional algorithms. Our primary algorithmic tool is the construction of an o(n)-sized implicit decomposition of a bounded-degree graph G on n nodes, which combined with read-only access to G enables fast answers to connectivity and biconnectivity queries on G. the construction breaks the linear-write "barrier", resulting in costs that are asymptotically lower than conventional algorithms while adding only a modest cost to querying time. For general non-sparse graphs on m edges, we also provide the first parallel algorithms for connectivity and biconnectivity that require o(m) writes and O(m) operations. these algorithms provide insight into how applications can efficiently process computations on large graphs in systems with read-write asymmetry.

关键词： Random access memory Computational modeling Task analysis parallel algorithms Writing Standards Clustering algorithms"

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