What are the advantages and disadvantages of using standard benchmarks for parallel computing?

1 What are standard benchmarks?

Standard benchmarks are predefined sets of problems or tasks that are designed to test the performance of a system or a program under certain conditions. For example, LINPACK is a standard benchmark that measures the floating-point performance of a system by solving a system of linear equations. Standard benchmarks can be classified into two categories: synthetic and application-based. Synthetic benchmarks are artificial problems that aim to stress a specific aspect of a system, such as memory bandwidth or communication overhead. Application-based benchmarks are real-world problems that reflect the typical workload of a system, such as image processing or scientific simulation.

2 Why use standard benchmarks?

Standard benchmarks have several advantages for parallel computing. First, they provide a common and objective way to measure and compare the performance of different parallel systems or programs. By using the same problem, input, and output, standard benchmarks can eliminate the effects of variations in hardware, software, or environment. Second, they can help identify the strengths and weaknesses of a parallel system or program. By analyzing the results of standard benchmarks, one can discover the bottlenecks, limitations, or opportunities for improvement of a parallel system or program. Third, they can facilitate the development and optimization of parallel systems or programs. By using standard benchmarks as reference points, one can test the correctness, efficiency, and scalability of a parallel system or program.

3 What are the limitations of standard benchmarks?

Standard benchmarks also have some disadvantages for parallel computing. First, they may not reflect the actual performance of a parallel system or program in real applications. Standard benchmarks may be too simple, too complex, or too specific to capture the diversity and dynamics of real-world problems. For example, a parallel system that performs well on LINPACK may not perform well on a different problem that requires more communication or synchronization. Second, they may not account for the variability and unpredictability of parallel systems or programs. Parallel systems or programs may behave differently depending on the number, type, and configuration of processors, machines, or networks. Standard benchmarks may not capture the effects of factors such as load balancing, fault tolerance, or resource contention. Third, they may not be updated or maintained regularly. Standard benchmarks may become obsolete or irrelevant as parallel systems or programs evolve and improve over time. For example, a parallel system that was ranked high on a standard benchmark a few years ago may not be competitive or compatible with newer parallel systems or programs.

4 How to use standard benchmarks effectively?

Standard benchmarks are useful tools for parallel computing, but they should not be used blindly or exclusively. To use standard benchmarks effectively, one should consider the following tips. First, choose the appropriate standard benchmarks for your parallel system or program. Depending on your goals, needs, and constraints, you may want to use synthetic or application-based benchmarks, or a combination of both. You may also want to use multiple standard benchmarks to cover different aspects of your parallel system or program. Second, understand the assumptions and limitations of the standard benchmarks you use. You should know how the standard benchmarks are designed, implemented, and evaluated, and what they can and cannot measure. You should also be aware of the sources of error, uncertainty, or bias in the standard benchmarks. Third, complement the standard benchmarks with other methods and metrics. You should not rely solely on standard benchmarks to assess the performance of your parallel system or program. You should also use other methods and metrics, such as analytical models, simulations, experiments, or user feedback, to validate, verify, and improve your parallel system or program.