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Signal/Collect Processing large graphs in seconds

SEMANTIC WEB 2016年第2期7卷 139-166页

作者： Stutz, Philip Strebel, Daniel Bernstein, Abraham Univ Zurich CH-8006 Zurich Switzerland

Both researchers and industry are confronted with the need to process increasingly large amounts of data, much of which has a natural graph representation. Some use MapReduce for scalable processing, but this abstraction is not designed for graphs and has shortcomings when it comes to both iterative and asynchronous processing, which are particularly important for graph algorithms. This paper presents the Signal/Collect programming model for scalable synchronous and asynchronous graph processing. We show that this abstraction can capture the essence of many algorithms on graphs in a concise and elegant way by giving Signal/Collect adaptations of algorithms that solve tasks as varied as clustering, inferencing, ranking, classification, constraint optimisation, and even query processing. Furthermore, we built and evaluated a parallel and distributed framework that executes algorithms in our programming model. We empirically show that our framework efficiently and scalably parallelises and distributes algorithms that are expressed in the programming model. We also show that asynchronicity can speed up execution times. Our framework can compute a PageRank on a large (>1.4 billion vertices, >6.6 billion edges) real-world graph in 112 seconds on eight machines, which is competitive with other graph processing approaches.

关键词： Distributed computing scalability programming abstractions programming models graph processing

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Exploiting functional directives to achieve MPI parallelization

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Civil-Comp Proceedings 2017年 111卷 1-19页

作者： Rubio Bonilla, D. Glass, C.W. HLRS University of Stuttgart Germany

In the last years CPU manufactures have not been able to substantially increase the Instructions Per Cycle of CPU cores. Trying to overcome this situation manufacturers have increased the raw performance of HPC systems by simultaneously increasing the amount of processors, by multiplying the number of cores in each processor and integrating specialized accelerators such as GPGPUs, FPGAs and other ASICs with specialized instruction sets. To be able to exploit the new hardware capabilities applications have to be specifically written with parallelism, to deal with the increasing number of cores available and also need to have parts of the source code written in specialized languages to make use of the integrated accelerators. This creates a major paradigm shift from compute centric to communication centric execution to which most programming models are not properly aligned yet: Classical models are geared towards optimizing the computational operations, assuming data access is almost for free. Languages like C, for example, assume that variables are immediately available and are accessed synchronously. The new situation implies that the data is going to be distributed across the system, and communication latency will have a big impact. A bad data distribution will result in a continue exchange while processing units are idling waiting for the data to compute. Most programming models do not convey the necessary dependency information to the compiler that must be careful not to make wrong assumptions. There are successful attempts, such as OpenMP, to exploit parallelism by introducing structural information of the application. Research projects, such as POLCA, have develop means to introduce functionallike semantics, in the form of directives, to procedural code that describe the structural behavior of the application with the aim to allow compilers to perform aggressive code transformations that increase performance and allow portability across different architectures.

关键词： C (programming language) Application programming interfaces (API) Codes (symbols) Cosine transforms Data flow analysis Energy utilization Functional programming Memory architecture Message passing Parallel processing systems Program compilers Semantics Computational operations Dependency informations Different architectures Hierarchical structures High performance computing Instructions per cycles Parallelizations programming models

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Evaluating Performance and Energy-Efficiency of a parallel Signal Correlation Algorithm on current Multi and Manycore Architectures

Evaluating Performance and Energy-Efficiency of a parallel S...

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16th Annual International Conference on Computational Science (ICCS)

作者： Hendricks, Arne Heller, Thomas Schaefer, Andreas Kasparek, Max Fey, Dietmar Friedrich Alexander Univ Erlangen Nurnberg EAU Dept Comp Architecture Dept Comp Sci Erlangen Germany Fraunhofer Inst Integrierte Schaltungen IIS Nurnberg Germany STE AR Grp Atlanta GA 30308 USA

Increasing variety and affordability of multi- and many-core embedded architectures can pose both a challenge and opportunity to developers of high performance computing applications. In this paper we present a case study where we develop and evaluate a unified parallel approach to a signal-correlation algorithm,currently in-use in a commercial/industrial locating system. We utilize both HPX C++ and CUDA runtimes to achieve scalable code for current embedded multi- and many-core architectures (NVIDIA Tegra, Intel Broadwell M, Arm Cortex A-15). We also compare our approach onto traditional high-performance hardware as well as a native embedded many-core variant. To increase the accuracy of our performance analysis we introduce dedicated performance model. The results show that our approach is feasible and enables us to harness the advantages of modern micro-server architectures, but also indicates that there are limitations to some of the currently existing many-core embedded architectures, that can lead to traditional hardware being superior both in efficiency and absolute performance.

关键词： programming models Many Core Runtimes User Feedback Micro Servers Shared Memory

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Comparing Runtime Systems with Exascale Ambitions Using the Parallel Research Kernels 31st

Comparing Runtime Systems with Exascale Ambitions Using the ...

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31st International Conference on ISC High Performance

作者： Van der Wijngaart, Rob F. Kayi, Abdullah Hammond, Jeff R. Jost, Gabriele St John, Tom Sridharan, Srinivas Mattson, Timothy G. Abercrombie, John Nelson, Jacob Intel Corp Hillsboro OR 97124 USA Univ Washington Seattle WA 98195 USA

ISBN: (纸本)9783319413211;9783319413204

We use three Parallel Research Kernels to compare performance of a set of programming models(We employ the term programming model as it is commonly used in the application community. A more accurate term is programming environment, which is the collective of abstract programming model, embodiment of the model in an Application Programmer Interface (API), and the runtime that implements it.): MPI1 (MPI two-sided communication), MPIOPENMP (MPI+OpenMP), MPISHM (MPI1 with MPI-3 interprocess shared memory), MPIRMA (MPI one-sided communication), SHMEM, UPC, Charm++ and Grappa. The kernels in our study -Stencil, Synch p2p and Transpose underlie a wide range of computational science applications. They enable direct probing of properties of programming models, especially communication and synchronization. In contrast to mini-or proxy applications, the PRK allow for rapid implementation, measurement and verification. Our experimental results show MPISHM the overall winner, with MPI1, MPIOPENMP and SHMEM performing well. MPISHM and MPIOPENMP outperform the other models in the strong-scaling limit due to their effective use of shared memory and good granularity control. The non-evolutionary models Grappa and Charm++ are not competitive with traditional models (MPI and PGAS) for two of the kernels;these models favor irregular algorithms, while the PRK considered here are regular.

关键词： programming models MPI PGAS Charm plus

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Exploiting Task-Parallelism in Message-Passing Sparse Linear System Solvers Using OmpSs 22nd

Exploiting Task-Parallelism in Message-Passing Sparse Linear...

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22nd International Conference on Parallel and Distributed Computing (Euro-Par)

作者： Aliaga, Jose I. Barreda, Maria Bollhoefer, Matthias Quintana-Orti, Enrique S. Univ Jaume 1 Dept Ingn & Ciencia Computadores Castellon de La Plana Spain TU Braunschweig Inst Computat Math Braunschweig Germany

ISBN: (纸本)9783319436593;9783319436586

We introduce a parallel implementation of the preconditioned iterative solver for sparse linear systems underlying ILUPACK that explores the interoperability between the message-passing MPI programming interface and the OmpSs task-parallel programming model. Our approach commences from the task dependency tree derived from a multilevel graph partitioning of the problem, and statically maps the tasks in the top levels of this tree to the cluster nodes, fixing the internode communication pattern. This mapping induces a conformal partitioning of the tasks in the remaining levels of the tree among the nodes, which are then processed concurrently via the OmpSs runtime system. The experimental analysis on a cluster with high-end Intel Xeon processors explores several configurations of MPI ranks and OmpSs threads per process showing that, in general, the best option matches the internal architecture of the nodes. The results also report significant performance gains for the MPI+OmpSs version over the initial MPI code.

关键词： programming models Sparse linear systems Preconditioned iterative solvers Task-level parallelism ILUPACK MPI OmpSs

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A Distributed OpenCL Framework using Redundant Computation and Data Replication 16

A Distributed OpenCL Framework using Redundant Computation a...

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37th ACM SIGPLAN Conference on programming Language Design and Implementation

作者： Kim, Junghyun Jo, Gangwon Jung, Jaehoon Kim, Jungwon Lee, Jaejin Seoul Natl Univ Ctr Manycore Programming Dept Comp Sci & Engn Seoul 08826 South Korea

ISBN: (纸本)9781450342612

Applications written solely in OpenCL or CUDA cannot execute on a cluster as a whole. Most previous approaches that extend these programming models to clusters are based on a common idea: designating a centralized host node and coordinating the other nodes with the host for computation. However, the centralized host node is a serious performance bottleneck when the number of nodes is large. In this paper, we propose a scalable and distributed OpenCL framework called SnuCL-D for large-scale clusters. SnuCL-D's remote device virtualization provides an OpenCL application with an illusion that all compute devices in a cluster are confined in a single node. To reduce the amount of control-message and data communication between nodes, SnuCL-D replicates the OpenCL host program execution and data in each node. We also propose a new OpenCL host API function and a queueing optimization technique that significantly reduce the overhead incurred by the previous centralized approaches. To show the effectiveness of SnuCL-D, we evaluate SnuCL-D with a microbenchmark and eleven benchmark applications on a large-scale CPU cluster and a medium-scale GPU cluster.

关键词： OpenCL clusters heterogeneous computing programming models runtime systems redundant computation data replication

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Towards Asynchronous Many-Task In Situ Data Analysis Using Legion 30

Towards Asynchronous Many-Task <i>In Situ</i> Data Analysis ...

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30th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Pebay, Philippe Bennett, Janine C. Hollman, David Treichler, Sean McCormick, Patrick S. Sweeney, Christine M. Kolla, Hemanth Aiken, Alex Sandia Natl Labs Livermore CA 94550 USA Stanford Univ Stanford CA 94305 USA Los Alamos Natl Lab Los Alamos NM USA

ISBN: (纸本)9781509036820

We explore the use of asynchronous many-task (AMT) programming models for the implementation of in situ analysis towards the goal of maximizing programmer productivity and overall performance on next generation platforms. We describe how a broad class of statistics algorithms can be transformed from a traditional single-programm multiple-data (SPMD) implementation to an AMT implementation, demonstrating with a concrete example: a measurement of descriptive statistics implemented in Legion. Our experiments to quantify the benefit and possible drawbacks of this approach are in progress, and we present some encouraging initial results on the (minimal) impact of the AMT-based approach on code complexity, task scheduling, and application scalability.

关键词： programming models Asynchronous Many-Task Parallel Computing Computational Statistics

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A Comparison of High-Level programming Choices for Incomplete Sparse Factorization Across Different Architectures 30

A Comparison of High-Level Programming Choices for Incomplet...

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30th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Booth, Joshua Dennis Kim, Kyungjoo Rajamanickam, Sivasankaran Sandia Natl Labs Ctr Res Comp POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781509036820

All many-core systems require fine-grained shared memory parallelism, however the most efficient way to extract such parallelism is far from trivial. Fine-grained parallel algorithms face various performance trade-offs related to tasking, accesses to global data-structures, and use of shared cache. While programming models provide high level abstractions, such as data and task parallelism, algorithmic choices still remain open on how to best implement irregular algorithms, such as sparse factorizations, while taking into account the trade-offs mentioned above. In this paper, we compare these performance trade-offs for task and data parallelism on different hardware architectures such as Intel Sandy Bridge, Intel Xeon Phi, and IBM Power8. We do this by comparing the scaling of a new task-parallel incomplete sparse Cholesky factorization called Tacho and a new data-parallel incomplete sparse LU factorization called Basker. Both solvers utilize Kokkos programming model and were developed within the ShyLU package of Trilinos. Using these two codes we demonstrate how high-level programming changes affect performance and overhead costs on multiple multi/many-core systems. We find that Kokkos is able to provide comparable performance with both parallel_for and task/futures on traditional x86 multicores. However, the choice of which high-level abstraction to use on many-core systems depends on both the architectures and input matrices.

关键词： Performance programming models Sparse Factorizations Sparse Linear Algebra

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Cutty: Aggregate Sharing for User-Defined Windows 16

Cutty: Aggregate Sharing for User-Defined Windows

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25th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Carbone, Paris Traub, Jonas Katsifodimos, Asterios Haridi, Seif Markl, Volker KTH Royal Inst Technol Stockholm Sweden Tech Univ Berlin Berlin Germany DFKI Saarbrucken Germany

ISBN: (纸本)9781450340731

Aggregation queries on data streams are evaluated over evolving and often overlapping logical views called windows. While the aggregation of periodic windows were extensively studied in the past through the use of aggregate sharing techniques such as Panes and Pairs, little to no work has been put in optimizing the aggregation of very common, non-periodic windows. Typical examples of non-periodic windows are punctuations and sessions which can implement complex business logic and are often expressed as user-defined operators on platforms such as Google Dataflow or Apache Storm. The aggregation of such non-periodic or user-defined windows either falls back to expensive, best-effort aggregate sharing methods, or is not optimized at all. In this paper we present a technique to perform efficient aggregate sharing for data stream windows, which are declared as user-defined functions (UDFs) and can contain arbitrary business logic. To this end, we first introduce the concept of User-Defined Windows (UDWs), a simple, UDF-based programming abstraction that allows users to programmatically define custom windows. We then define semantics for UDWs, based on which we design Cutty, a low-cost aggregate sharing technique. Cutty improves and outperforms the state of the art for aggregate sharing on single and multiple queries. Moreover, it enables aggregate sharing for a broad class of non-periodic UDWs. We implemented our techniques on Apache Flink, an open source stream processing system, and performed experiments demonstrating orders of magnitude of reduction in aggregation costs compared to the state of the art.

关键词： data structures data stream aggregation programming models operator sharing data stream processing data streams data stream optimisation data stream windows functional programming user-defined functions databases

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Energy-Aware programming Model for Distributed Infrastructures 24

Energy-Aware Programming Model for Distributed Infrastructur...

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24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP)

作者： Lordan, Francesc Ejarque, Jorge Sirvent, Raul Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain Univ Politecn Cataluna E-08028 Barcelona Spain CSIC Artificial Intelligence Res Inst IIIA Madrid Spain

ISBN: (纸本)9781467387767

Day after day, cloud technologies are more and more adopted by very diverse types of stakeholders, and this success creates a side-effect problem: the energy spent by this kind of infrastructures is growing bigger every day. With the objective of reducing energy consumption when programming applications for cloud infrastructures, we have implemented energy-aware mechanisms in the COMPSs programming Model, inside the context of the ASCETiC Project. In this paper, we demonstrate that application-level scheduling can have a big impact on the energy consumed by an application when executed in a heterogeneous cloud. We have implemented an energy-aware scheduling mechanism in COMPSs, together with a versioning technique, and we have run experiments with a use case coming from the real estate sector that proves our hypotheses.

关键词： programming models distributed computing energy awareness cloud computing

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