An Analytic Method for Estimating the Computation Capacity of Computing Devices

Self Cite

The notion of computer capacity was proposed in 2012, and this quantity has been estimated for computers of different kinds. In this paper we show that, when designing new processors, the manufacturers change the parameters that affect the computer capacity. This allows us to predict the values of parameters of future processors. As the main example we use Intel processors, due to the accessibility of detailed description of all their technical characteristics.

Section: Introductionsupporting

confidence: 78%

Section: Introductionsupporting

confidence: 78%

Section: The Computer Capacity Of Intel Processorsmentioning

confidence: 97%

See 1 more Smart Citation

Application of the Computer Capacity to the Analysis of Processors Evolution

Ryabko

Rakitskiy

2019

Self Cite

“…Later, it was applied to real computers with Intel processors. 2 This method is closed in spirit to the main concept of Shannon's information theory 3 and it uses the characteristic Computer Capacity as a measure of performance. In this paper, we describe the application of the suggested method to performance evaluation of Haswell processors and three supercomputers based on them.…”

Section: Introductionmentioning

confidence: 99%

Theoretical Approach to Performance Evaluation of Supercomputers

Ryabko

Rakitskiy

2017

Self Cite

In this paper, we extend an information-theoretic approach of computer performance evaluation to supercomputers. This approach is based on the notion of computer Capacity which can be estimated relying solely on the description of computer architecture. We describe the method of calculating Computer Capacity for supercomputers including the influence of the architecture of communication network. The suggested approach is applied to estimate the performance of three of the top 10 supercomputers (according to TOP500 June-2016 list) which are based on Haswell processors. For greater objectivity of results, we compared them relatively to values of another supercomputer which is based an Ivy Bridge processors (this microarchitecture differs from Haswell). The obtained results are compared with values of TOP500 LINPACK benchmark and theoretical peak and we arrive at conclusions about the applicability of the presented theoretical approach (nonexperimental) for performance evaluation of real supercomputers. In particular, it means that the estimations of the computer capacity can be used at the design stage of the development of supercomputers.

“…They combine performance modeling with benchmarking, and construct efficiency models with smaller error rates. Ryabko et.al 30 introduce computer capacity metric by providing equations to estimate the performance of a processor. We use a statistical performance model instead of an analytical approach for performance prediction due to the complex runtime options of task-based programs.…”

mentioning

confidence: 99%

Regression-Based Prediction for Task-Based Program Performance

Bhatti

Popov

et al. 2019

As multicore systems evolve by increasing the number of parallel execution units, parallel programming models have been released to exploit parallelism in the applications. Task-based programming model uses task abstractions to specify parallel tasks and schedules tasks onto processors at runtime. In order to increase the efficiency and get the highest performance, it is required to identify which runtime configuration is needed and how processor cores must be shared among tasks. Exploring design space for all possible scheduling and runtime options, especially for large input data, becomes infeasible and requires statistical modeling. Regression-based modeling determines the effects of multiple factors on a response variable, and makes predictions based on statistical analysis. In this work, we propose a regression-based modeling approach to predict the task-based program performance for different scheduling parameters with variable data size. We execute a set of task-based programs by varying the runtime parameters, and conduct a systematic measurement for influencing factors on execution time. Our approach uses executions with different configurations for a set of input data, and derives different regression models to predict execution time for larger input data. Our results show that regression models provide accurate predictions for validation inputs with mean error rate as low as 6.3%, and 14% on average among four task-based programs.