Measuring parallel processor performance
Communications of the ACM
A bridging model for parallel computation
Communications of the ACM
Algorithmic skeletons: structured management of parallel computation
Algorithmic skeletons: structured management of parallel computation
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
Statistical Models in S
Computer Methods for Mathematical Computations
Computer Methods for Mathematical Computations
Introduction to Parallel Computing
Introduction to Parallel Computing
Isoefficiency: Measuring the Scalability of Parallel Algorithms and Architectures
IEEE Parallel & Distributed Technology: Systems & Technology
Parallel functional programming at two levels of abstraction
Proceedings of the 3rd ACM SIGPLAN international conference on Principles and practice of declarative programming
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
Parallel functional programming in Eden
Journal of Functional Programming
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
The Scalasca performance toolset architecture
Concurrency and Computation: Practice & Experience - Scalable Tools for High-End Computing
Implementing data parallel rational multiple-residue arithmetic in eden
CASC'10 Proceedings of the 12th international conference on Computer algebra in scientific computing
Estimating parallel performance
Journal of Parallel and Distributed Computing
| Hi-index | 0.00 |
In this paper we estimate parallel execution times, based on identifying separate "parts" of the work done by parallel programs. We assume that programs are described using algorithmic skeletons. Therefore our runtime 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. This allows us to predict parallel execution time for unknown inputs and non-available processor numbers. Another useful application of our formalism is a measure of parallel program quality. We analyse the values for parallel penalty both for growing input size and for increasing numbers of processing elements. From these data, conclusions on parallel performance and scalability are drawn.