Performance modeling and simulation of parallel applications are critical performance analysis techniques in High Performance
Computing (HPC). Efficient and accurate performance modeling and simulation can aid the tuning and optimization of current systems as well as
the design of future HPC systems. As the HPC applications and systems increase in size, efficient and accurate performance modeling and
simulation of parallel applications is becoming increasingly challenging. In general, simulation yields higher accuracy at the cost of high
simulation time in comparison to modeling. This dissertation aims at developing effective performance analysis techniques for the next
generation HPC systems. Since modeling is often orders of magnitude faster than simulation, the idea is to separate HPC applications into two
types: 1) the ones that modeling can produce similar performance results as simulation and 2) the ones that simulation can result in more
meaningful information about the application performance than modeling. By using modeling for the first type of applications and simulation
for the rest of applications, the efficiency of performance analysis can be significantly improved. The contribution of this thesis is
three-fold. First, a comprehensive study of the performance and accuracy trade-offs between modeling and simulation on a wide range of HPC
applications is performed. The results indicate that for the majority of HPC applications, modeling and simulation yield similar performance
results. This lays the foundation for improving performance analysis on HPC systems by selecting between modeling and simulation on each
application. Second, a scalable and fast classification techniques (MFACT) are developed based on the Lamport's logical clock that can
provide fast diagnosis of MPI application performance bottleneck and assist in the processing of application tuning and optimization on
current and future HPC systems. MFACT also classifies HPC applications into bandwidth-bound, latency-bound, communication-bound, and
computation-bound. Third, built-upon MFACT, for a given system configuration, statistical methods are introduced to classify HPC applications
into the two types: the ones that needs simulation and the ones that modeling is sufficient. The classification techniques and tools enable
effective performance analysis for future HPC systems and applications without losing accuracy. / A Dissertation submitted to the Department of Computer Science in partial fulfillment of the requirements
for the degree of Doctor of Philosophy. / Fall Semester 2017. / November 6, 2017. / Application, Communication, HPC, Performance modeling, performance simulation / Includes bibliographical references. / Xin Yuan, Professor Directing Dissertation; Fengfeng Ke, University Representative; Zhenghao Zhang,
Committee Member; Sonia Haiduc, Committee Member; Scott Pakin, Committee Member.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_605024 |
| Contributors | Tong, Zhou (author), Yuan, Xin (professor directing dissertation), Ke, Fengfeng (university representative), Zhang, Zhenghao (committee member), Haiduc, Sonia (committee member), Pakin, Scott D. (committee member), Florida State University (degree granting institution), College of Arts and Sciences (degree granting college), Department of Computer Science (degree granting departmentdgg) |
| Publisher | Florida State University |
| Source Sets | Florida State University |
| Language | English, English |
| Detected Language | English |
| Type | Text, text, doctoral thesis |
| Format | 1 online resource (85 pages), computer, application/pdf |
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