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Multi-core Portability Abstraction

Multi-core Portability Abstraction

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

作者： Forsell, Martti Hiivala, Mikko VTT Tech Res Ctr Finland Platform Architectures Team FI-90571 Oulu Finland

ISBN: (纸本)9780769546766

Application portability between different multicore architecture-parallel programming paradigm/tool pairs is a big problem nowadays leading often to a complete rewrite of an application when switching from an architecture-paradigm pair to another. This is caused by a wide variety of architectural properties requiring different optimization techniques for different architectures, typically hiding the essence of (parallel) computation defined by the application. In this paper, we introduce the Multi-Core Portability Abstraction (MCPA) simplifying portability and implementation of parallel applications making use of shared memory. It abstracts away typical architecture dependent effects caused by latency, synchronization, and partitioning and acts as an executable intermediate abstraction/reference implementation as well as a tool for analyzing the intrinsic parallelism of the application and relative goodness of architectures in executing it. We give a short application example with performance measurements.

关键词： parallel computing multicore architectures model of computation parallel programming application portability

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Partitioning for parallel Matrix-Matrix Multiplication with Heterogeneous Processors: The Optimal Solution

Partitioning for Parallel Matrix-Matrix Multiplication with ...

引用

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

作者： DeFlumere, Ashley Lastovetsky, Alexey Becker, Brett A. Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland

ISBN: (纸本)9780769546766

The problem of matrix partitioning for parallel matrix-matrix multiplication on heterogeneous processors has been extensively studied since the mid 1990s. During this time, previous research focused mainly on the design of efficient partitioning algorithms, optimally or sub-optimally partitioning matrices into rectangles. The optimality of the rectangular partitioning shape itself has never been studied or even seriously questioned. The accepted approach is that consideration of non-rectangular shapes will not significantly improve the optimality of the solution, but can significantly complicate the partitioning problem, which is already NP-complete even for the restricted case of rectangular shapes. There is no published research, however, supporting this approach. The shape of the globally optimal partitioning, and how the best rectangular partitioning compares with this global optimum, are still wide open problems. Solution of these problems will decide if new partitioning algorithms searching for truly optimal, and not necessarily rectangular, solutions are needed. This paper presents the first results of our research on the problem of optimal partitioning shapes for parallel matrix-matrix multiplication on heterogeneous processors. Namely, the case of two interconnected processors is comprehensively studied. We prove that, depending on performance characteristics of the processors and the communication link, the globally optimal partitioning will have one of just two well-specified shapes, one of which is rectangular and the other is non-rectangular. The theoretical analysis is conducted using an original mathematical technique proposed in the paper. It is shown that the technique can also be applied in the case of arbitrary numbers of processors. While comprehensive analysis of the cases of three and more processors is more complicated and the subject for future work, the paper does prove the optimality of some particular non-rectangular partitioning shapes fo

关键词： parallel Matrix Multiplication Matrix Partitioning Heterogeneous computing High Performance computing

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Hierarchical Local Storage: Exploiting Flexible User-Data Sharing Between MPI Tasks

Hierarchical Local Storage: Exploiting Flexible User-Data Sh...

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

作者： Tchiboukdjian, Marc Carribault, Patrick Perache, Marc Exascale Comp Res Versailles France

ISBN: (纸本)9780769546759

With the advent of the multicore era, the number of cores per computational node is increasing faster than the amount of memory. This diminishing memory to core ratio sometimes even prevents pure MPI applications to exploit all cores available on each node. A possible solution is to add a shared memory programming model like OpenMP inside the application to share variables between OpenMP threads that would otherwise be duplicated for each MPI task. Going to hybrid can thus improve the overall memory consumption, but may be a tedious task on large applications. To allow this data sharing without the overhead of mixing multiple programming models, we propose an MPI extension called Hierarchical Local Storage (HLS) that allows application developers to share common variables between MPI tasks on the same node. HLS is designed as a set of directives that preserve the original parallel semantics of the code and are compatible with C, C++ and Fortran languages and the OpenMP programming model. This new mechanism is implemented inside a state-of-the-art MPI 1.3 compliant runtime called MPC. Experiments show that the HLS mechanism can effectively reduce memory consumption of HPC applications. Moreover, by reducing data duplication in the shared cache of modern multicores, the HLS mechanism can also improve performances of memory intensive applications.

关键词： High Performance computing parallel Programming Model Memory Consumption

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A fault-aware sensor architecture for cooperative mobile applications

A fault-aware sensor architecture for cooperative mobile app...

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

作者： Kaiser, Joerg Zug, Sebastian Univ Magdeburg Dept Distributed Syst D-39106 Magdeburg Germany

ISBN: (纸本)9780769546766

One of the striking characteristics of mobile embedded systems is the interaction with the physical world. Prerequisite for this interaction is the reliable perception of the environment by sensors. Exploiting remote sensors of an instrumented environment and other mobile systems will extend the range and modalities of sensing and, in principle, will contribute to a better environment perception. However, such as distributed sensor system also puts new substantial challenges on the assessment and dynamic use of sensor data. This paper focus on dependability issues and presents a fault abstraction and fault-handling concept, that encapsulates individual sensor faults and encourages multi-level fault detection. Based on an analysis of sensor faults, our approach generates a validation for each sensor measurement. This allows assessing remote sensor data quality in a uniform way and provides the basis for a distributed mobile application in which new sensor sources can be discovered and exploited dynamically.

关键词： Fault detection Fault abstraction Fault model distributed application Smart sensors

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Analysis and Optimization of Data Import with Hadoop

Analysis and Optimization of Data Import with Hadoop

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

作者： Xu, Weijia Luo, Wei Woodward, Nicholas Univ Texas Austin Texas Adv Comp Ctr Austin TX 78712 USA Beijing Document Serv Beijing Peoples R China Univ Texas Austin Sch Informat Austin TX 78712 USA

ISBN: (纸本)9780769546766

Data driven research has become an important part of scientific discovery in an increasing number of disciplines. In many cases, the sheer volume of data to be processed requires not only state-of-the-art computing resources but also carefully tuned and specifically developed software. These requirements are often associated with huge operational costs and significant expertise in software development. Due to its simplicity for the user and effectiveness at processing big data, Hadoop has become a popular software platform for large-scale data analysis. Using a Hadoop cluster in a remote shared infrastructure enables users to avoid the costs of maintaining a physical infrastructure. An inevitable step in using dynamically constructed Hadoop cluster is the initial importing of the data. This process is not trivial, particularly when the size of the data is large. In this paper, we evaluate the costs of importing large-scale data into a Hadoop cluster. We present a detailed analysis of the default data importing implementation in Hadoop and conduct a practical evaluation. Our evaluation includes tests with different hardware configurations, such as different network protocol and disk configurations. We also propose an implementation to improve the performance of importing data into a Hadoop cluster wherein the data is accessed directly by Datanodes during the import process.

关键词： cloud computing data import disk I/O data transfter Hadoop

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An approach to parallelize Kruskal's algorithm using Helper Threads

An approach to parallelize Kruskal's algorithm using Helper ...

引用

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

作者： Katsigiannis, Anastasios Anastopoulos, Nikos Nikas, Konstantinos Koziris, Nectarios Natl Tech Univ Athens Sch Elect & Comp Engn Comp Syst Lab GR-10682 Athens Greece

ISBN: (纸本)9780769546766

In this paper we present a Helper Threading scheme used to parallelize efficiently Kruskal's Minimum Spanning Forest algorithm. This algorithm is known for exhibiting inherently sequential characteristics. More specifically, the strict order by which the algorithm checks the edges of a given graph is the main reason behind the lack of explicit parallelism. Our proposed scheme attempts to overcome the imposed restrictions and improve the performance of the algorithm. The results show that for a wide range of graphs of varying structure, size and density the parallelization of Kruskal's algorithm is feasible. Observed speedups reach up to 5.5 for 8 running threads, revealing the potentials of our approach.

关键词： Kruskal's Algorithm Minimum Spanning Forest parallel algorithms Helper Threads

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SWAPP: A Framework for Performance Projections of HPC Applications Using Benchmarks

SWAPP: A Framework for Performance Projections of HPC Applic...

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

作者： Sharkawi, Sameh DeSota, Don Panda, Raj Stevens, Stephen Taylor, Valerie Wu, Xingfu IBM Corp Syst & Technol Grp Austin TX USA Texas A&M Univ Dept Comp Sci & Engn College Stn TX 77843 USA

ISBN: (纸本)9780769546766

Surrogate-based Workload Application Performance Projection (SWAPP) is a framework for performance projections of High Performance computing (HPC) applications using benchmark data. Performance projections of HPC applications onto various hardware platforms are important for hardware vendors and HPC users. The projections aid hardware vendors in the design of future systems and help HPC users with system procurement. SWAPP assumes that one has access to a base system and only benchmark data for a target system;the target system is not available for running the HPC application. Projections are developed using the performance profiles of the benchmarks and application on the base system and the benchmark data for the target system. SWAPP projects the performances of compute and communication components separately then combine the two projections to get the full application projection. In this paper SWAPP was used to project the performance of three NAS Multi-Zone benchmarks onto three systems (an IBM POWER6 575 cluster and an IBM Intel Westmere x5670 both using an Infiniband interconnect and an IBM BlueGene/P with a 3D Torus and Collective Tree interconnects);the base system is an IBM POWER5+ 575 cluster. The projected performance of the three benchmarks was within 11.44% average error magnitude and standard deviation of 2.64% for the three systems.

关键词： BENCHMARKS High Performance computing Hardware Platform IBM communication components

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Using Localization for Fault-tolerant Radio Coverage in Wireless Mesh Networks

Using Localization for Fault-tolerant Radio Coverage in Wire...

引用

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

作者： Ivanov, Svilen Nett, Edgar Rt Solut S GmbH Cologne Germany Univ Magdeburg Inst Distributed Syst D-39106 Magdeburg Germany

ISBN: (纸本)9780769546766

Wireless Mesh Networks offer flexibility for industrial automation. However, in environments with changing propagation conditions it is challenging to guarantee the radio coverage. This paper contributes a localization-based method to achieve fault-tolerant radio coverage of wireless mesh networks in dynamic propagation environments. The idea is to use radio signal strength measurements from the mobile stations, find their positions with localization and use this information to automatically calibrate a radio propagation model to the real environment. This model is then used to automatically detect radio coverage errors at run-time and to repair them before the radio coverage fails. An evaluation in a real industrial scenario shows the practicability of our approach.

关键词： Localization Wireless Networks Radio Coverage Model Calibration

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Cloud Services Gateway: A tool for exposing Private Services to the Public Cloud with fine-grained Control

Cloud Services Gateway: A tool for exposing Private Services...

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

作者： Perera, Srinath Kumarasiri, Rajika Kamburugamuva, Supun Fernando, Senaka Weerawarana, Sanjiva Fremantle, Paul WSO2 Inc Colombo Sri Lanka

ISBN: (纸本)9780769546766

By enabling users to allocate computing resources on demand, cheaply, and in an elastic manner, Cloud computing has made large computation resources available to small and medium size organizations. However, using the Cloud requires users to place their computations, data, or both in a shared data center own by an outsider. This sharing has raised many security concerns. Such concerns are much apparent with use cases like health informatics, where the security of the information is critical and imposed by government regulations. We propose a hybrid approach to solve this problem, where only computations are moved to the public domains while keeping the data within the private network, and computations may access data through a set of services that expose data following the Principle of Least Privilege. Such architectures will, however, require computations in the cloud to connect to the local network that holds the data, and the obvious solution: that is opening up ports in the organizational firewall could potentially create security loopholes. As an alternative, we propose Cloud Services Gateway (CSG), which enable users to selectively expose their private services that reside inside a firewall to outside clients while maintaining fine grained control. This paper motivates hybrid Cloud architectures and presents the architecture and design decisions of Cloud Services Gateway.

关键词： cloud computing security

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Load Balancing of Dynamical Nucleation Theory Monte Carlo Simulations Through Resource Sharing Barriers

Load Balancing of Dynamical Nucleation Theory Monte Carlo Si...

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

作者： Arafat, Humayun Sadayappan, P. Dinan, James Krishnamoorthy, Sriram Windus, Theresa L. Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Pacific Northwest Natl Lab Comp Sci & Math Div Richland WA 99354 USA Iowa State Univ Dept Chem Ames IA 50011 USA

ISBN: (纸本)9780769546759

The dynamical nucleation theory Monte Carlo (DNTMC) application from the NWChem computational chemistry suite utilizes a Markov chain Monte Carlo, two-level parallel structure, with periodic synchronization points that assemble the results of independent finer-grained calculations. Like many such applications, the existing code employs a static partitioning of processes into groups and assigns each group a piece of the finer-grained parallel calculation. A significant cause of performance degradation is load imbalance among groups since the time requirements of the inner-parallel calculation varies widely with the input problem and as a result of the Monte Carlo simulation. We present a novel approach to load balancing such calculations with minimal changes to the application. We introduce the concept of a resource sharing barrier (RSB) - a barrier that allows process groups waiting on other processes' work to actively contribute to their completion. The RSB load balancing technique is applied to the production DNTMC application code, resulting in a small code change of 200 lines and a reduction in execution time of up to 37%.

关键词： Monte Carlo Simulation parallel processing Load balancing Resource management Global Arrays PGAS

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