ACM Transactions on Computer Systems (TOCS)
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
Performance prediction of parallel processing systems: the PAMELA methodology
ICS '93 Proceedings of the 7th international conference on Supercomputing
Precise compile-time performance prediction for superscalar-based computers
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
Analyzing the behavior and performance of parallel programs
Analyzing the behavior and performance of parallel programs
Object-oriented simulation modeling with C++/CSIM17
WSC '95 Proceedings of the 27th conference on Winter simulation
Static performance prediction of data-dependent programs
Proceedings of the 2nd international workshop on Software and performance
The distributed ASCI Supercomputer project
ACM SIGOPS Operating Systems Review
Performance Evaluation of Computer and Communication Systems, Joint Tutorial Papers of Performance '93 and Sigmetrics '93
A probabilistic approach to parallel system performance modelling
HICSS '95 Proceedings of the 28th Hawaii International Conference on System Sciences
Integrated Compilation and Scalability Analysis for Parallel Systems
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
| Hi-index | 0.00 |
Symbolic cost models are an important performance engineering tool because of their diagnostic value and their very low solution cost when the computation features regularity. However, especially for parallel applications their derivation, including the symbolic simplifications essential for low solution cost, is an effort-intensive and error-prone process. We present a tool that automatically compiles process-oriented performance simulation models into symbolic cost models that are symbolically simplified to achieve extremely low solution cost. As the simulation models are intuitively close to the parallel program and machine under study, derivation effort is significantly reduced. Apart from its use as a stand-alone tool, the compiler is also used within a symbolic cost estimator for data-parallel programs. With minimal program annotation by the user, symbolic cost models are automatically generated in a matter of seconds, while the evaluation time of the models ranges in the milliseconds. Experimental results on four data-parallel programs show that the average prediction error is less than 15 %. Apart from providing program scalability assessment, the models correctly predict the best design alternative in all cases.