High Performance Computing at IBM Research - India - overview
The High Performance Computing (HPC) group at IBM Research - India is engaged in designing and analyzing cutting edge parallel programs and improving the performance of engineering, scientific, and business applications on high performance platforms such as the IBM Blue Gene supercomputer. The group has following major focus areas:
- Parallel scalable algorithms and benchmarks for supercomputers
- Optimization of application performance on multi-core processors, large-scale supercomputers, and clusters
- Environment and Renewable Energy Modelling
Following sections provide more details on the research projects in the group.
Parallel scalable algorithms and benchmarks for supercomputers
The HPC Challenge (HPCC) and Graph500 benchmarks are used for evaluating the performance of supercomputers across a spectrum of real-world applications. The HPC team at IBM Research - India is actively involved in performance optimization and tuning of these benchmarks on high-end systems, such as the Blue Gene supercomputers and POWER based systems. The optimizations include designing new algorithms, data structures and other intricate techniques for distributed memory and multicore architectures. This group has optimized the STREAM, RandomAccess, Fast Fourier Transform, Transpose and Single Source Shortest Path benchmarks for various systems. The HPC Challenge RandomAccess benchmark optimized by this team has won the HPCC Class I award at Supercomputing for 6 years (2005-2010).
Optimization of application performance on multi-core processors, large-scale supercomputers, and clusters
The HPC team is also involved in optimizing various scientific applications that require high-end systems for large scale processing. These applications include molecular dynamics simulation packages such as VASP, weather modeling packages, smart grid applications such as contingency analysis, etc. on the Blue Gene supercomputers.
Environment and Renewable Energy Modelling
The HPC team also works on the modelling, parallelization and optimization of environment science applications such as weather forecasting, climate forecasting, hydrology modelling and agriculture. The team also work on combining the strengths of high resolution weather models and statistical machine learning methods to forecast wind and solar power availability.