Measuring parallel processor performance
Communications of the ACM
A bridging model for parallel computation
Communications of the ACM
LogP: towards a realistic model of parallel computation
PPOPP '93 Proceedings of the fourth ACM SIGPLAN symposium on Principles and practice of parallel programming
Analyzing scalability of parallel algorithms and architectures
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
Programming parallel algorithms
Communications of the ACM
Analysis of benchmark characteristics and benchmark performance prediction
ACM Transactions on Computer Systems (TOCS)
Statistical Models in S
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures
IEEE Parallel & Distributed Technology: Systems & Technology
Parallelism abstractions in eden
Patterns and skeletons for parallel and distributed computing
Parallelism in random access machines
STOC '78 Proceedings of the tenth annual ACM symposium on Theory of computing
Predictive Application-Performance Modeling in a Computational Grid Environment
HPDC '99 Proceedings of the 8th IEEE International Symposium on High Performance Distributed Computing
Parallel functional programming in Eden
Journal of Functional Programming
Extended forecast of CPU and network load on computational Grid
CCGRID '04 Proceedings of the 2004 IEEE International Symposium on Cluster Computing and the Grid
Amdahl's Law in the Multicore Era
Computer
Validity of the single processor approach to achieving large scale computing capabilities
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Visualizing massively multithreaded applications with ThreadScope
Concurrency and Computation: Practice & Experience
Riemann's hypothesis and tests for primality
Journal of Computer and System Sciences
The Scalasca performance toolset architecture
Concurrency and Computation: Practice & Experience - Scalable Tools for High-End Computing
Estimating parallel performance, a skeleton-based approach
Proceedings of the fourth international workshop on High-level parallel programming and applications
Implementing data parallel rational multiple-residue arithmetic in eden
CASC'10 Proceedings of the 12th international conference on Computer algebra in scientific computing
An approach to performance prediction for parallel applications
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
Parallel computation skeletons with premature termination property
FLOPS'12 Proceedings of the 11th international conference on Functional and Logic Programming
Self-Configuration and Self-Optimization Autonomic Skeletons using Events
Proceedings of Programming Models and Applications on Multicores and Manycores
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In this paper we introduce our estimation method for parallel execution times, based on identifying separate ''parts'' of the work done by parallel programs. Our run time analysis works without any source code inspection. The time of parallel program execution is expressed in terms of the sequential work and the parallel penalty. We measure these values for different problem sizes and numbers of processors and estimate them for unknown values in both dimensions using statistical methods. This allows us to predict parallel execution time for unknown inputs and non-available processor numbers with high precision. Our prediction methods require orders of magnitude less data points than existing approaches. We verified our approach on parallel machines ranging from a multicore computer to a peta-scale supercomputer. Another useful application of our formalism is a new measure of parallel program quality. We analyse the values for parallel penalty for both growing input size and for increasing numbers of processing elements. From these data, conclusions on parallel performance and scalability are drawn.