We developed a Functional object-oriented Parallel framework (FooPar) for high-level high-performance computing in Scala. Central to this framework are Distributed Memory Parallel Data structures (DPDs), i.e., collections of data distributed in a shared nothing system together with parallel operations on these data. In this paper, we first present FooPar's architecture and the idea of DPDs and group communications. Then, we show how DPDs can be implemented elegantly and efficiently in Scala based on the Traversable Builder pattern, unifying Functional and Object-Oriented Programming. We prove the correctness and safety of one communication algorithm and show how specification testing (via ScalaCheck) can be used to bridge the gap between proof and implementation. Furthermore, we show that the group communication operations of FooPar outperform those of the MPJ Express open source MPI-bindings for Java, both asymptotically and empirically. FooPar has already been shown to be capable of achieving close-to-optimal performance for dense matrix-matrix multiplication via JNI. In this article, we present results on a parallel implementation of the Floyd-Warshall algorithm in FooPar, achieving more than 94 efficiency compared to the serial version on a cluster using 100 cores for matrices of dimension 38000 x 38000.
While complements the @cite_18 introduced in Scala 2.8, it is not meant as an extension. This is due to multiple reasons. First, the parallel collections use workload-splitting strategies leading to communication bottle-necks in distributed memory settings. Second, they employ an implicit master-slave paradigm unsuitable for massively distributed HPC. Third, the SPMD paradigm requires launching multiple copies of the process as opposed to branching internally into threads.
@cite_18
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To solve the limitation of Hadoop on scalability, resource sharing, and application support, the open-source community proposes the next generation of Hadoop's compute platform called Yet Another Resource Negotiator YARN by separating resource management functions from the programming model. This separation enables various application types to run on YARN in parallel. To achieve fair resource sharing and high resource utilization, YARN provides the capacity scheduler and the fair scheduler. However, the performance impacts of the two schedulers are not clear when mixed applications run on a YARN cluster. Therefore, in this paper, we study four scheduling-policy combinations SPCs for short derived from the two schedulers and then evaluate the four SPCs in extensive scenarios, which consider not only four application types, but also three different queue structures for organizing applications. The experimental results enable YARN managers to comprehend the influences of different SPCs and different queue structures on mixed applications. The results also help them to select a proper SPC and an appropriate queue structure to achieve better application execution performance. Copyright © 2016 John Wiley & Sons, Ltd. An extremely scalable computational strategy is described for calculations of the potential of mean force (PMF) in multidimensions on massively distributed supercomputers. The approach involves coupling thousands of umbrella sampling (US) simulation windows distributed to cover the space of order parameters with a Hamiltonian molecular dynamics replica-exchange (H-REMD) algorithm to enhance the sampling of each simulation. In the present application, US H-REMD is carried out in a two-dimensional (2D) space and exchanges are attempted alternatively along the two axes corresponding to the two order parameters. The US H-REMD strategy is implemented on the basis of parallel parallel multiple copy protocol at the MPI level, and therefore can fully exploit computing power of large-scale supercomputers. Here the novel technique is illustrated using the leadership supercomputer IBM Blue Gene P with an application to a typical biomolecular calculation of general interest, namely the binding of calcium ions to the ... As high-end computer systems present users with rapidly increasing numbers of processors, possibly also incorporating attached co-processors, programmers are increasingly challenged to express the necessary levels of concurrency with the dominant parallel programming model, Fortran+MPI+OpenMP (or minor variations). In this paper, we examine the languages developed under the DARPA High-Productivity Computing Systems (HPCS) program (Chapel, Fortress, and XIO) as representatives of a different parallel programming model which might be more effective on emerging high-performance systems. The application used in this study is the Hartree-Fock method from quantum chemistry, which combines access to distributed data with a task-parallel algorithm and is characterized by significant irregularity in the computational tasks. We present several different implementation strategies for load balancing of the task-parallel computation, as well as distributed array operations, in each of the three languages. We conclude that the HPCS languages provide a wide variety of mechanisms for expressing parallelism, which can be combined at multiple levels, making them quite expressive for this problem. We are witnessing a dramatic change in computer architecture due to the multicore paradigm shift, as every electronic device from cell phones to supercomputers confronts parallelism of unprecedented scale. To fully unleash the potential of these systems, the HPC community must develop multicore specific optimization methodologies for important scientific computations. In this work, we examine sparse matrix-vector multiply (SpMV) - one of the most heavily used kernels in scientific computing - across a broad spectrum of multicore designs. Our experimental platform includes the homogeneous AMD dual-core and Intel quad-core designs, the heterogeneous STI Cell, as well as the first scientific study of the highly multithreaded Sun Niagara2. We present several optimization strategies especially effective for the multicore environment, and demonstrate significant performance improvements compared to existing state-of-the-art serial and parallel SpMV implementations. Additionally, we present key insights into the architectural tradeoffs of leading multicore design strategies, in the context of demanding memory-bound numerical algorithms.