Right-weight kernels: an off-the-shelf alternative to custom light-weight kernels
ACM SIGOPS Operating Systems Review
Holistic aggregate resource environment
ACM SIGOPS Operating Systems Review
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Characterizing application sensitivity to OS interference using kernel-level noise injection
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Motivating future interconnects: a differential measurement analysis of PCI latency
Proceedings of the 5th ACM/IEEE Symposium on Architectures for Networking and Communications Systems
Characterizing the Influence of System Noise on Large-Scale Applications by Simulation
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Performance analysis of parallel programs via message-passing graph traversal
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Linux kernel co-scheduling for bulk synchronous parallel applications
Proceedings of the 1st International Workshop on Runtime and Operating Systems for Supercomputers
Linux kernel co-scheduling and bulk synchronous parallelism
International Journal of High Performance Computing Applications
Software—Practice & Experience
NIX: A Case for a Manycore System for Cloud Computing
Bell Labs Technical Journal
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Microbenchmarks, i.e. very small computational kernels, have become commonly used for quantitative measures of node performance in clusters. For example, a commonly used benchmark measures the amount of time required to perform a fixed quantum of work. Unfortunately, this benchmark is one of many that violate well known rules from sampling theory, leading to erroneous, contradictory or misleading results. At a minimum, these types of benchmarks can not be used to identify time-based activities that may interfere with and hence limit application performance. Our original and primary goal remains to identify noise in the system due to periodic activities that are not part of user application code. We discuss why the 'fixed quantum of work' benchmark provides data that is of limited use for analysis; and we show code for, discuss, and analyze results from a microbenchmark which follows good rules of sampling hygiene, and hence provides useful data for analysis.