Kim, Youngtae, Fienup, Mark, Clary, Jeffrey C. and Kothari, Suresh C. (1994) Parametric Micro-level Performance Models for Parallel Computing. Technical Report TR94-23, Department of Computer Science, Iowa State University.
Parametric Micro-level Performance Models
for Parallel Computing
Youngtae Kim, Mark Fienup, Jeffrey S. Clary, and Suresh C. Kothari
Parametric micro-level (PM) performance models are introduced to address
the important issue of how to realistically model parallel performance.
These models can be used to predict execution times, identify performance
bottlenecks, and compare machines. The accurate prediction and analysis
of execution times is achieved by incorporating precise details of
interprocessor communication, memory operations, auxiliary instructions,
and effects of communication and computation schedules. Parameters are
used for flexibility to study various algorithmic and architectural issues.
The development and verification process, parameters and the scope of
applicability of these models are discussed. A coherent view of performance
is obtained from the execution profiles generated by PM models. The models
are targeted at a large class numerical algorithms commonly implemented
on both SIMD and MIMD machines. Specific models are presented for matrix
multiplication, LU decomposition, and FFT on a 2-D processor array with
distributed memory. A case study is done on MasPar MP-1 and MP-2 machines
to validate PM models and demonstrate their utility.
Contact site administrator at: firstname.lastname@example.org