70,783 research outputs found
Distributed Maple: parallel computer algebra in networked environments
AbstractWe describe the design and use of Distributed Maple, an environment for executing parallel computer algebra programs on multiprocessors and heterogeneous clusters. The system embeds kernels of the computer algebra system Maple as computational engines into a networked coordination layer implemented in the programming language Java. On the basis of a comparatively high-level programming model, one may write parallel Maple programs that show good speedups in medium-scaled environments. We report on the use of the system for the parallelization of various functions of the algebraic geometry library CASA and demonstrate how design decisions affect the dynamic behaviour and performance of a parallel application. Numerous experimental results allow comparison of Distributed Maple with other systems for parallel computer algebra
Vcluster: A Portable Virtual Computing Library For Cluster Computing
Message passing has been the dominant parallel programming model in cluster computing, and libraries like Message Passing Interface (MPI) and Portable Virtual Machine (PVM) have proven their novelty and efficiency through numerous applications in diverse areas. However, as clusters of Symmetric Multi-Processor (SMP) and heterogeneous machines become popular, conventional message passing models must be adapted accordingly to support this new kind of clusters efficiently. In addition, Java programming language, with its features like object oriented architecture, platform independent bytecode, and native support for multithreading, makes it an alternative language for cluster computing. This research presents a new parallel programming model and a library called VCluster that implements this model on top of a Java Virtual Machine (JVM). The programming model is based on virtual migrating threads to support clusters of heterogeneous SMP machines efficiently. VCluster is implemented in 100% Java, utilizing the portability of Java to address the problems of heterogeneous machines. VCluster virtualizes computational and communication resources such as threads, computation states, and communication channels across multiple separate JVMs, which makes a mobile thread possible. Equipped with virtual migrating thread, it is feasible to balance the load of computing resources dynamically. Several large scale parallel applications have been developed using VCluster to compare the performance and usage of VCluster with other libraries. The results of the experiments show that VCluster makes it easier to develop multithreading parallel applications compared to conventional libraries like MPI. At the same time, the performance of VCluster is comparable to MPICH, a widely used MPI library, combined with popular threading libraries like POSIX Thread and OpenMP. In the next phase of our work, we implemented thread group and thread migration to demonstrate the feasibility of dynamic load balancing in VCluster. We carried out experiments to show that the load can be dynamically balanced in VCluster, resulting in a better performance. Thread group also makes it possible to implement collective communication functions between threads, which have been proved to be useful in process based libraries
A Flexible Patch-Based Lattice Boltzmann Parallelization Approach for Heterogeneous GPU-CPU Clusters
Sustaining a large fraction of single GPU performance in parallel
computations is considered to be the major problem of GPU-based clusters. In
this article, this topic is addressed in the context of a lattice Boltzmann
flow solver that is integrated in the WaLBerla software framework. We propose a
multi-GPU implementation using a block-structured MPI parallelization, suitable
for load balancing and heterogeneous computations on CPUs and GPUs. The
overhead required for multi-GPU simulations is discussed in detail and it is
demonstrated that the kernel performance can be sustained to a large extent.
With our GPU implementation, we achieve nearly perfect weak scalability on
InfiniBand clusters. However, in strong scaling scenarios multi-GPUs make less
efficient use of the hardware than IBM BG/P and x86 clusters. Hence, a cost
analysis must determine the best course of action for a particular simulation
task. Additionally, weak scaling results of heterogeneous simulations conducted
on CPUs and GPUs simultaneously are presented using clusters equipped with
varying node configurations.Comment: 20 pages, 12 figure
Near-Optimal Straggler Mitigation for Distributed Gradient Methods
Modern learning algorithms use gradient descent updates to train inferential
models that best explain data. Scaling these approaches to massive data sizes
requires proper distributed gradient descent schemes where distributed worker
nodes compute partial gradients based on their partial and local data sets, and
send the results to a master node where all the computations are aggregated
into a full gradient and the learning model is updated. However, a major
performance bottleneck that arises is that some of the worker nodes may run
slow. These nodes a.k.a. stragglers can significantly slow down computation as
the slowest node may dictate the overall computational time. We propose a
distributed computing scheme, called Batched Coupon's Collector (BCC) to
alleviate the effect of stragglers in gradient methods. We prove that our BCC
scheme is robust to a near optimal number of random stragglers. We also
empirically demonstrate that our proposed BCC scheme reduces the run-time by up
to 85.4% over Amazon EC2 clusters when compared with other straggler mitigation
strategies. We also generalize the proposed BCC scheme to minimize the
completion time when implementing gradient descent-based algorithms over
heterogeneous worker nodes
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