Alex BreslowComputer Science PhD Student
University of California, San Diego
La Jolla, CA
I received my Master of Science in Computer Science.
I will intern at Microsoft Research Silicon Valley from June 30 to October 3. I hope to post more details soon.
I am particularly interested in research internships. I enjoy traveling, so I encourage you to contact me even if you are situated outside the United States. If language skills beyond English are required, I speak good Spanish and decent French.
My paper entitled Enabling Fair Pricing on HPC Systems with Node Sharing has been accepted by The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC'13) and is a finalist for the Best Paper Award and the Best Student Paper Award. Ananta Tiwari, Martin Schulz, Laura Carrington, Lingjia Tang, and Jason Mars are coauthors.
My SC13 paper has been highlighted on HPCwire.com! link
I am currently looking for an advisor. Please contact me if you have any suggestions.
For my citation counts, metrics and those of my co-authors, check out my Google Scholar profile.
Alex D. Breslow, Ananta Tiwari, Martin Schulz, Laura Carrington, Lingjia Tang, and Jason Mars. Enabling Fair Pricing on HPC Systems with Node Sharing. To appear in the Proceedings of SC'13. Best Paper Finalist and Best Student Paper Finalist[pdf|bib]
Alex D. Breslow, Leo Porter, Ananta Tiwari, Michael A. Laurenzano, Laura Carrington, Dean M. Tullsen, and Allan E. Snavely. The Case for Colocation of HPC Workloads. Concurrency and Computation: Practice and Experience; Special issue on the Analysis of Performance and Power for Highly Parallel Systems (CCPE 2013). [pdf]|[bib]
Andrew Danner, Jake Baskin, Alexander Breslow, and David Wilikofsky. Hybrid MPI/GPU Interpolation for Grid DEM Construction. In Proc. ACM Symposium on Advances in Geographic Information Systems, pages 299-308, 2012. [pdf|bib]
I am deeply interested in the area of general computing systems, as these systems pose many challenges and because their efficient operation is imperative for a productive society. As more and more computation moves to centralized data centers, the efficiency and throughput of these systems will determine fundamentally what is computable, and thus what we as a society can achieve.
My recent research has attacked the large scale system research question from an High Performance Computing (HPC) perspective. However, I am widening my research scope to address challenges found in the commercial data center realm. My goal is to develop algorithmic and software mechanisms that are general enough to span both scientific and enterprise cloud infrastructures.
In addition to these current endeavors, I am interested in areas relating to hardware-software codesign and the art of elegant parallel programming, as advances in these fields are now the principal mechanism by which we can continue to get significant increases in performance every 18 or so months with Moore's Law. Besides these aspects of the parallel programming challenge, I continue to be fascinated by the design of novel parallel algorithms that allow us to crunch through big data by harnessing the computing power of distributed computing environments.
I currently work with the folks at the Performance Modeling and Characterization Lab at SDSC, and as of January 2013 have begun joint work with Martin Schulz at Lawrence Livermore National Laboratory on projects related to exascale computing. The exceptional Allan Snavely was my first PhD advisor at UCSD; however, on July 14, 2012, he tragically passed away from a heart attack shortly after cycling up Mount Diablo in Northern California. I will always remember his kindly nature and be ever grateful for his unrelenting support of my growth as a researcher.
My work with Allan Snavely identified that there is a substantial performance and energy efficiency benefit to colocating distributed HPC applications from different users across a set of shared servers. This work will appear in the research journal Concurrency and Computation: Practice and Experience. I am currently extending this work and am tackling the two main challenges involved in realizing colocation on supercomputers: establishing fair pricing and deciding which applications should corun. To address the fair pricing challenge, I have developed a light-weight runtime system that prices relative to the performance degradation that an application experiences. The first part of this work has recently been accepted to SC'13 and has been nominated for both the Best Paper and Best Student Paper awards. This work is a collaboration with Jason Mars and Lingjia Tang who are now faculty at University of Michigan and with whom I had the excellent privilege of working with during their time at UCSD.
I would like to thank Tia Newhall and Andrew Danner of Swarthmore College for piquing my interest in parallel and distributed computing.
I thank Professor Doug Turnbull, now at Ithaca College, for his encouragement and mentoring both during and after my data structures and algorithms course at Swarthmore.
At UCSD, I would be remiss if I did not acknowledge the guidance of the following individuals: Allan Snavely, Ananta Tiwari, Dean Tullsen, Jason Mars, Laura Carrington, Lingjia Tang, Leo Porter and Michael Laurenzano. Each has contributed to my growth as a researcher.
I would also like to thank all those who have advocated on my behalf throughout the years. Without their help, I would surely not be where I am today.
This page layout has been graciously provided by Professor Andrew Danner at Swarthmore College.