nqing Yu

Performance prediction of large-scale parallell system and application using macro-level simulation

To predict application performance on an HPC system is an important technology for designing the computing system and developing applications. However, accurate prediction is a challenge, particularly, in the case of a future coming system with higher performance. In this paper, we present a new method for predicting application performance on HPC systems. This method combines modeling of sequential performance on a single processor and macro-level simulations of applications for parallel performance on the entire system. In the simulation, the execution flow is traced but kernel computations are omitted for reducing the execution time. Validation on a real terascale system showed that the predicted and measured performance agreed within 10% to 20 %. We employed the method in designing a hypothetical petascale system of 32768 SIMD-extended processor cores. For predicting application performance on the petascale system, the macro-level simulation required several hours.

Reconfigurable neural network using DAP/DNA

We want to find ways of realizing a neural network in DAP/DNA. As convenient platform for experiments the DAP/DNA were taken, which allows the change of hardware in one clock per application, flexibly and dynamically. Implementing neural network by using this method, high computational power will be provided, and flexibility and scalability are hold.