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Using Mpi Portable Parallel Programming With The Message Passing Interface

https://olcf.ornl.gov/wp-content/uploads/2011/10/hybrid.pdf

Implementing molecular dynamics on hybrid high performance computers - short range forces

Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey

This is a thought piece on data-intensive science requirements for databases and science centers. It argues that peta-scale datasets will be housed by science centers that provide substantial storage and processing for scientists who access the data via smart notebooks. Next-generation science instruments and simulations will generate these peta-scale datasets. The need to publish and share data and the need for generic analysis and visualization tools will finally create a convergence on common metadata standards. Database systems will be judged by their support of these metadata standards and by their ability to manage and access peta-scale datasets. The procedural stream-of-bytes-file-centric approach to data analysis is both too cumbersome and too serial for such large datasets. Non-procedural query and analysis of schematized self-describing data is both easier to use and allows much more parallelism.

Scientific Data Management in the Coming Decade

Thanks to advances in wide-area network technologies and the low cost of computing resources, Grid computing came into being and is currently an active research area. One motivation of Grid computing is to aggregate the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal, an efficient Grid scheduling system is an essential part of the Grid. Rather than covering the whole Grid scheduling area, this survey provides a review of the subject mainly from the perspective of scheduling algorithms. In this review, the challenges for Grid scheduling are identified. First, the architecture of components involved in scheduling is briefly introduced to provide an intuitive image of the Grid scheduling process. Then various Grid scheduling algorithms are discussed from different points of view, such as static vs. dynamic policies, objective functions, applications models, adaptation, QoS constraints, strategies dealing with dynamic behavior of resources, and so on. Based on a comprehensive understanding of the challenges and the state of the art of current research, some general issues worthy of further exploration are proposed.

/pdf/scheduling-algorithms-for-grid-computing-state-of-the-art-2ds7x5glks.pdf

Scheduling Algorithms for Grid Computing: State of the Art and Open Problems

Live migration of virtual machines has been a powerful tool to facilitate system maintenance, load balancing, fault tolerance, and power-saving, especially in clusters or data centers. Although pre-copy is a predominantly used approach in the state of the art, it is difficult to provide quick migration with low network overhead, due to a great amount of transferred data during migration, leading to large performance degradation of virtual machine services. This paper presents the design and implementation of a novel memory-compression-based VM migration approach (MECOM) that first uses memory compression to provide fast, stable virtual machine migration, while guaranteeing the virtual machine services to be slightly affected. Based on memory page characteristics, we design an adaptive zero-aware compression algorithm for balancing the performance and the cost of virtual machine migration. Pages are quickly compressed in batches on the source and exactly recovered on the target. Experiment demonstrates that compared with Xen, our system can significantly reduce 27.1% of downtime, 32% of total migration time and 68.8% of total transferred data on average.

Live virtual machine migration with adaptive, memory compression

MapReduceis emerging as an important programming model for large scale parallel application. Meanwhile, Hadoop is an open source implementation of MapReduce enjoying wide popularity for developing data intensive applications in the cloud. As, in the cloud, the computing unit is virtual machine (VM) based; it is feasible to demonstrate the applicability of MapReduce on virtualized data center. Although the potential for poor performance and heavy load no doubt exists, virtual machines can instead be used to fully utilize the system resources, ease the management of such systems, improve the reliability, and save the power. In this paper, a series of experiments are conducted to measure and analyze the performance of Hadoop on VMs. Our experiments are used as a basis for outlining several issues that will need to be considered when implementing MapReduce to fit completely in the cloud.

Evaluating MapReduce on Virtual Machines: The Hadoop Case

In the big data era, much real-world data can be naturally represented as graphs. Consequently, many application domains can be modeled as graph processing. Graph processing, especially the processing of the large-scale graphs with the number of vertices and edges in the order of billions or even hundreds of billions, has attracted much attention in both industry and academia. It still remains a great challenge to process such large-scale graphs. Researchers have been seeking for new possible solutions. Because of the massive degree of parallelism and the high memory access bandwidth in GPU, utilizing GPU to accelerate graph processing proves to be a promising solution. This article surveys the key issues of graph processing on GPUs, including data layout, memory access pattern, workload mapping, and specific GPU programming. In this article, we summarize the state-of-the-art research on GPU-based graph processing, analyze the existing challenges in detail, and explore the research opportunities for the future.

/pdf/graph-processing-on-gpus-a-survey-2rl24d7te7.pdf

Graph Processing on GPUs: A Survey

In recent years, deep learning has gained unprecedented success in various domains, the key of the success is the larger and deeper deep neural networks (DNNs) that achieved very high accuracy. On the other side, since GPU global memory is a scarce resource, large models also pose a significant challenge due to memory requirement in the training process. This restriction limits the DNN architecture exploration flexibility. In this paper, we propose Capuchin, a tensor-based GPU memory management module that reduces the memory footprint via tensor eviction/prefetching and recomputation. The key feature of Capuchin is that it makes memory management decisions based on dynamic tensor access pattern tracked at runtime. This design is motivated by the observation that the access pattern to tensors is regular during training iterations. Based on the identified patterns, one can exploit the total memory optimization space and offer the fine-grain and flexible control of when and how to perform memory optimization techniques. We deploy Capuchin in a widely-used deep learning framework, Tensorflow, and show that Capuchin can reduce the memory footprint by up to 85% among 6 state-of-the-art DNNs compared to the original Tensorflow. Especially, for the NLP task BERT, the maximum batch size that Capuchin can outperforms Tensorflow and gradient-checkpointing by 7x and 2.1x, respectively. We also show that Capuchin outperforms vDNN and gradient-checkpointing by up to 286% and 55% under the same memory oversubscription.

Xuanhua Shi

Papers

Live virtual machine migration with adaptive, memory compression

Evaluating MapReduce on Virtual Machines: The Hadoop Case

Graph Processing on GPUs: A Survey

Capuchin: Tensor-based GPU Memory Management for Deep Learning

Optimizing the live migration of virtual machine by CPU scheduling