by John Barr, Research Director, The 451 Group
Writing and implementing high performance computing applications is all about efficiency, parallelism, scalability, cache optimizations and making best use of whatever resources are available — be they multicore processors or application accelerators, such as FPGAs or GPUs. HPC applications have been developed for, and successfully run on, grids for many years now.
A good example of a number of different components of HPC applications can be seen in the processing of data from CERN’s Large Hadron Collider (LHC). The LHC is a gigantic scientific instrument (with a circumference of over 26 kilometres), buried underground near Geneva. Data is collected by a number of “experiments.” each of which is a large and very delicate collection of sensors able to capture the side effects caused by exotic, short lived particles that result from the particle collisions. When accelerated to full speed, the bunches of particles pass each other 40 million times a second, each bunch contains 10^11 particles, resulting in one billion collision events being detected every second. This data is first filtered by a system build from custom ASIC and FPGA devices. It is then processed by a 1,000 processor compute farm, and the filtering is completed by a 3,400 processor farm. After the data has been reduced by a factor of 180,000, it still generates 3,200 terabytes of data a year. And the HPC processing undertaken to reduce the data volume has hardly scratched the surface of what happens next.
