CLUSTER 2009 - PDF Preprint Papers

CLUSTER 2009 IEEE International Conference on Cluster Computing

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CLUSTER
2009
IEEE

663views Distributed And Parallel Com...» more CLUSTER 2009»

MDCSim: A multi-tier data center simulation, platform

14 years 5 months ago

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Abstract—Performance and power issues are becoming increasingly important in the design of large cluster based multitier data centers for supporting a multitude of services. Desi...

Seung-Hwan Lim, Bikash Sharma, Gunwoo Nam, Eun-Kyo...

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Id	Views	Title	Status

1	663	MDCSim: A multi-tier data center simulation, platform	claim
2	307	SJMR: Parallelizing spatial join with MapReduce on clusters	claim
3	258	Accelerating SIFT on parallel architectures	claim
4	252	Live virtual machine migration with adaptive, memory compression	claim
5	243	Message passing for GPGPU clusters: CudaMPI	claim
6	203	Analyzing massive astrophysical datasets: Can Pig/Hadoop or a relational DBMS help?	claim
7	199	Granules: A lightweight, streaming runtime for cloud computing with support, for Map-Reduce	claim
8	178	Analytical modeling and optimization for affinity based thread scheduling on multicore systems	claim
9	170	HPMR: Prefetching and pre-shuffling in shared MapReduce computation environment	claim
10	169	Design alternatives for implementing fence synchronization in MPI-2 one-sided communication for InfiniBand clusters	claim
11	166	Coordinating the use of GPU and CPU for improving performance of compute intensive applications	claim
12	163	MITHRA: Multiple data independent tasks on a heterogeneous resource architecture	claim
13	161	Power-aware scheduling of virtual machines in DVFS-enabled clusters	claim
14	161	24/7 Characterization of petascale I/O workloads	claim
15	154	DCR: A fully transparent checkpoint/restart framework for distributed systems	claim
16	151	An application based MPI message throughput benchmark	claim
17	148	Computing median values in a cloud environment using GridBatch and MapReduce	claim
18	141	A scalable and generic task scheduling system for communication libraries	claim
19	138	Topics on measuring real power usage on high performance computing platforms	claim
20	137	Numerically stable, single-pass, parallel statistics algorithms	claim
21	137	Using a cluster as a memory resource: A fast and large virtual memory on MPI	claim
22	133	Increasing the availability provided by RADIC with low overhead	claim
23	133	Integrating software distributed shared memory and message passing programming	claim
24	133	Combining I/O operations for multiple array variables in parallel netCDF	claim
25	133	Investigating the use of GPU-accelerated nodes for SAR image formation	claim
26	132	GPU clusters for high-performance computing	claim
27	132	A performance evaluation of scientific I/O workloads on Flash-based SSDs	claim
28	130	A compiler for high performance computing with many-core accelerators	claim
29	129	Resource selection and allocation for dynamic adaptive computing in heterogeneous clusters	claim
30	128	Utility analysis for Internet-oriented server consolidation in VM-based data centers	claim
31	122	An innovative application execution toolkit for multicluster grids	claim
32	122	Reliability-aware scalability models for high performance computing	claim
33	120	V-MCS: A configuration system for virtual machines	claim
34	119	Performance analysis of memory transfers and GEMM subroutines on NVIDIA Tesla GPU cluster	claim
35	119	Oblivious routing schemes in extended generalized Fat Tree networks	claim
36	117	Two-phase load distribution for rendering large 3D models on a graphics cluster	claim
37	117	Finding a tradeoff between host interrupt load and MPI latency over Ethernet	claim
38	114	Using clouds for metagenomics: A case study	claim
39	114	A global operating system for HPC clusters	claim
40	112	Batch queue resource scheduling for workflow applications	claim
41	109	Overlapping computation and communication in SMT clusters with commodity interconnects	claim
42	106	Comparing map-reduce and FREERIDE for data-intensive applications	claim
43	105	RDMA over Ethernet - A preliminary study	claim
44	99	Improving I/O performance using soft-QoS-based dynamic storage cache partitioning	claim
45	95	Extending I/O through high performance data services	claim
46	93	An efficient hardware-software approach to network fault tolerance with InfiniBand	claim
47	91	Reducing network contention with mixed workloads on modern multicore, clusters	claim
48	89	Scalable I/O forwarding framework for high-performance computing systems	claim
49	87	Integrating Web 2.0 technologies with scientific simulation codes for real-time collaboration	claim
50	72	Fault-aware, utility-based job scheduling on Blue, Gene/P systems	claim

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