Understanding the Impact of Multi-Core Architecture in Cluster Computing: A Case Study with Intel Dual-Core System

Chai, Lei; Gao, Qi; Panda, D.K.

doi:10.1109/ccgrid.2007.119

Cited by 122 publications

(68 citation statements)

References 7 publications

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“…To overcome the limitations of single core processors, which rapidly reach the physical limits of possible complexity and speed, currently multi-core processors are becoming the new industry trend. In view of applicability, a multi-core processor has acquired more than 20% of the new Top 500 supercomputer list [15]. The shifting trend to decrease the size of chips while increasing the number of transistors that they contain has led to enhanced computer performance and reduction in cost.…”

Section: A Implementation Using Multi-core Processormentioning

confidence: 99%

Evaluation of Performance on Open MP Parallel Platform based on Problem Size

Dash¹,

Kumar²,

Patle³

2016

IJMECS

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Abstract-This paper evaluates the performance of matrix multiplication algorithm on dual core 2.0 GHz processor with two threads. A novel methodology was designed to implement this algorithm on Open MP platform by selecting time of execution, speed up and efficiency as performance parameters. Based on the experimental analysis, it was found that a good performance can be achieved by executing the problem in parallel rather than sequential after a certain problem size.

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Section: A Implementation Using Multi-core Processormentioning

confidence: 99%

Evaluation of Performance on Open MP Parallel Platform based on Problem Size

Dash¹,

Kumar²,

Patle³

2016

IJMECS

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“…Adoption of multicore processors in clusters introduces non-uniform communication costs [2,13]. In such a cluster the cost of inter-node communication can be an order of magnitude higher than for intra-node communication.…”

Section: Related Workmentioning

confidence: 99%

Orthrus: A Framework for Implementing Efficient Collective I/O in Multi-core Clusters

Zhang

Davis

et al. 2014

Lecture Notes in Computer Science

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Abstract. Optimization of access patterns using collective I/O imposes the overhead of exchanging data between processes. In a multi-core-based cluster the costs of inter-node and intra-node data communication are vastly different, and heterogeneity in the efficiency of data exchange poses both a challenge and an opportunity for implementing efficient collective I/O. The opportunity is to effectively exploit fast intra-node communication. We propose to improve communication locality for greater data exchange efficiency. However, such an effort is at odds with improving access locality for I/O efficiency, which can also be critical to collective-I/O performance. To address this issue we propose a framework, Orthrus, that can accommodate multiple collective-I/O implementations, each optimized for some performance aspects, and dynamically select the best performing one accordingly to current workload and system patterns. We have implemented Orthrus in the ROMIO library. Our experimental results with representative MPI-IO benchmarks on both a small dedicated cluster and a large production HPC system show that Orthrus can significantly improve collective I/O performance under various workloads and system scenarios.

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“…Chai et al [30] study the impact of the Intel multicore architecture on the performance of various applications by looking at the amount of intra-node and inter-node communication performed.…”

Section: Early Hardware Asymmetrymentioning

confidence: 99%

Runtime Adaptation for Autonomic Heterogeneous Computing

Scogland

Feng

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity. I have also had the good fortune to collaborate with both Lawrence Livermore National Laboratory and Argonne National Laboratory. Experiences as an intern at each lab has had a significant effect on the final shape of my dissertation. Special thanks go to Dr. Pavan Balaji (again), Dr. Bronis de Supinski (again) and Dr. Barry Rountree. These collaborations proved to be major turning points for me, both in my research and my life as a whole.

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Understanding the Impact of Multi-Core Architecture in Cluster Computing: A Case Study with Intel Dual-Core System

Cited by 122 publications

References 7 publications

Evaluation of Performance on Open MP Parallel Platform based on Problem Size

Evaluation of Performance on Open MP Parallel Platform based on Problem Size

Orthrus: A Framework for Implementing Efficient Collective I/O in Multi-core Clusters

Runtime Adaptation for Autonomic Heterogeneous Computing

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