A performance estimation model for high-performance computing on clouds

2012 ◽  
Author(s):  
Jih-Sheng Chang ◽  
Ruay-Shiung Chang
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Performance Estimation ◽  
Estimation Model ◽  
Performance Estimation Model ◽  
Performance Computing ◽  
A Performance

Analytical Performance Estimation for Large-Scale Reconfigurable Dataflow Platforms

2021 ◽  
Vol 14 (3) ◽  
pp. 1-21
Author(s):  
Ryota Yasudo ◽  
José G. F. Coutinho ◽  
Ana-Lucia Varbanescu ◽  
Wayne Luk ◽  
Hideharu Amano ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Heterogeneous Systems ◽  
Performance Estimation ◽  
Performance Impact ◽  
Accurate Performance ◽  
Computing Platforms ◽  
Reduced Power Consumption ◽  
Performance Computing

Next-generation high-performance computing platforms will handle extreme data- and compute-intensive problems that are intractable with today’s technology. A promising path in achieving the next leap in high-performance computing is to embrace heterogeneity and specialised computing in the form of reconfigurable accelerators such as FPGAs, which have been shown to speed up compute-intensive tasks with reduced power consumption. However, assessing the feasibility of large-scale heterogeneous systems requires fast and accurate performance prediction. This article proposes Performance Estimation for Reconfigurable Kernels and Systems (PERKS), a novel performance estimation framework for reconfigurable dataflow platforms. PERKS makes use of an analytical model with machine and application parameters for predicting the performance of multi-accelerator systems and detecting their bottlenecks. Model calibration is automatic, making the model flexible and usable for different machine configurations and applications, including hypothetical ones. Our experimental results show that PERKS can predict the performance of current workloads on reconfigurable dataflow platforms with an accuracy above 91%. The results also illustrate how the modelling scales to large workloads, and how performance impact of architectural features can be estimated in seconds.

A performance estimation model for AS/RS by M/G/1 queuing system

2004 ◽  
Vol 46 (2) ◽  
pp. 233-241 ◽  
Author(s):  
Sun Hur ◽  
Young Hae Lee ◽  
Si Yeong Lim ◽  
Moon Hwan Lee
Keyword(s):  
Queuing System ◽  
Performance Estimation ◽  
Estimation Model ◽  
Performance Estimation Model ◽  
A Performance
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