Electronic Structure Calculations and Adaptation Scheme in Multi-core Computing Environments

Multi-core processing environments have become the norm in the generic computing environment and are being considered for adding an extra dimension to the execution of any application. The T2 Niagara processor is a very unique environment where it consists of eight cores having a capability of running eight threads simultaneously in each of the cores. Applications like General Atomic and Molecular Electronic Structure (GAMESS), used for ab-initio molecular quantum chemistry calculations, can be good indicators of the performance of such machines and would be a guideline for both hardware designers and application programmers. In this paper we try to benchmark the GAMESS performance on a T2 Niagara processor for a couple of molecules. We also show the suitability of using a middleware based adaptation algorithm on GAMESS on such a multi-core environment

adaptations in electronic structure calculations in heterogeneous environments

Modern quantum chemistry deals with electronic structure calculations of unprecedented complexity and accuracy. They demand full power of high-performance computing and must be in tune with the given architecture for superior efficiency. To make such applications resource-aware, it is desirable to enable their static and dynamic adaptations using some external software (middleware), which may monitor both system availability and application needs, rather than mix science with system-related calls inside the application. The present work investigates scientific application interlinking with middleware based on the example of the computational chemistry package GAMESS and middleware NICAN. The existing synchronous model is limited by the possible delays due to the middleware processing time under the sustainable runtime system conditions. Proposed asynchronous and hybrid models aim at overcoming this limitation. When linked with NICAN, the fragment molecular orbital (FMO) method is capable of adapting statically and dynamically its fragment scheduling policy based on the computing platform conditions. Significant execution time and throughput gains have been obtained due to such static adaptations when the compute nodes have very different core counts. Dynamic adaptations are based on the main memory availability at run time. NICAN prompts FMO to postpone scheduling certain fragments, if there is not enough memory for their immediate execution. Hence, FMO may be able to complete the calculations whereas without such adaptations it aborts

Performance analysis and middleware assisted adaptation for quantum chemistry computations

Quantum chemistry applications such as General Atomic and Molecular Electronic Structure System(GAMESS) that can execute on a complex peta-scale parallel computing environment have a large number of input parameters that affect the overall performance. The application characteristics vary according to the input parameters. This is due to the difference in the usage of resources like network bandwidth, I/O and main memory according to the input parameters. Effective execution of applications in a parallel computing environment that share such resources require some sort of adaptive mechanism to enable efficient usage of these resources. The adaptation adjusts the most computationally intensive part of the application thus leading to sizable gains. General Atomic and Molecular Electronic Structure System (GAMESS), used for ab-initio molecular quantum chemistry calculations, utilizes NICAN (Network Information Conveyer and Application Notification) for dynamically making adaptations so as to improve the application performance in heavy load conditions. The adaptation mechanism has the ability to modify the application execution in a very simplistic yet effective manner. In this work, we have explored methods to expand the structure of NICAN in order to include other input parameters based on which the application performance can be controlled. The application performance has been analyzed on different architectures to otain fine grained performance data and a tuning strategy has been identified. A generic database framework has been incorporated in the existing NICAN mechanism