The High Performance Computing Center (HPCC) at Stanford University is an entirely self sustaining academic service center run primarily by undergraduate students. This means that undergraduate students get an intensive learning experience and have the opportunity to work on production HPC systems. This has a significant positive impact on their future careers, as can be seen by the testimonial of one of our graduated students. He says:

“Working at the HPCC has been an invaluable experience during my college career for multiple reasons. In terms of my undergraduate career, the HPCC provided me with a daily commitment that I believe allowed me to develop crucial skills outside of academia. I found myself gaining a breadth of different experiences and developing skills in areas that couldn’t be covered by coursework. Steve allowed me to take on projects that were both rewarding in their difficulty, yet liberating in the amount of autonomy that I had in working on them. I found myself managing and collaborating with other students and faculty on projects that I was ultimately responsible for, and this was one of the most valuable aspects of my years there. For this reason, I found myself excited to come to work each day. The diversity of fields I was able to involve myself in thanks to the HPCC, ranging from cluster management to high-performance computing was also a great addition to the relatively narrow space that I was able to explore in pursuing my degree alone. While I don’t work in the same space during my day-to-day job, the technical skills I was able to hone while working at the HPCC are vital to where I am today as a software engineer. That’s why I can say with complete confidence that working at the HPCC was one of the most valuable aspects of my college career.”

In addition to collaborating productively with HPC group members and computational scientists, undergraduate students in the group conduct joint research on software profiling and optimization projects (currently with IBM). Our mission is to support the research efforts of scientists performing sponsored research.

To do this, we evaluate, acquire, and deploy high performance computing resources, including both computing systems and software. In addition, we offer training workshops, seminars, and lectures in academic classes to educate and prepare new users of these resources. We also provide expert consulting and technical documentation to assist researchers in using these resources effectively, conduct research and testing of new computational techniques and technologies that enhance the capabilities of high performance computing resources, and collaborate with national labs, computing centers and High Performance Computing vendors to enhance the capabilities of the resources in use at the center.

We use High Performance Computing (HPC) systems of a variety of architectures to enable larger simulations, analyses and faster computation times than are possible using computers available to individual researchers. We have Advanced Scientific Visualization resources including computing systems with high performance graphics hardware, large displays, display walls, and high-end pre and post processing facilities to enable large data analysis and promote discovery. We have Massive Data Storage/Archival systems to store the vast quantities of data that result from performing simulations on HPC systems and developing visualizations of large data sets.

All of these resources are put towards furthering research with many and varied real world applications.

For example, our researchers were the first to set a new record in supercomputing. They broke the million-core barrier, harnessing a million computing cores to model supersonic jet noise. That data will be used to help design quieter jet engines. More information about this can be found here.


Stanford awarded American Recovery and Reinvestment Act funding as part of NSF MRI-R2

The Stanford High Performance Computing Center has a new cluster, named Certainty. The cluster is part of a $6 million dollar proposal submitted to the NSF MRI-R2 program, award funded by the American Recovery and Reinvestment Act.