2015 Hot Topics in High-Performance Distributed Computing Workshop - overview
Thursday, March 12, 2015
IBM Almaden Research Center (Auditorium A)
High-Performance Distributed Computing (HPDC) combines the advances in research and technologies in high speed networks, software, distributed computing and parallel processing to deliver high-performance, large-scale and cost-effective computational, storage and communication capabilities to a wide range of applications. In this unique meeting, HPDC thought leaders report on new ideas, their visions and technical insights.
This workshop is open to the HPDC15 organizers and the entire IBM community.
Links to Talks
Richard Vuduc - Georgia Institute of Technology
How much time, energy, and power does my algorithm need?
Dick Epema - Delft and Eindhoven University of Technology
An Update from Delft: Scheduling Workflows, MapReduce, and More
Douglas Thain - University of Notre Dame
Portability and Preservation of Scientific Applications
David Abramson - University of Queensland
High Performance Parallel Debugging
Carlos Maltzahn - University of California, Santa Cruz
CROSSing the gap between systems research at universities and open source software projects
Michela Taufer - University of Deleware
Enabling Scalable Data Analysis of Large Computational Structural Biology Datasets on Distributed Memory Systems
Matei Ripeanu - University of British Columbia
Accelerating Graph Processing on Hybrid Systems
Ana Varbanescu - University of Amsterdam
On the Performance of Parallel Algorithms for Graph Processing
Wuchun Feng - Virginia Tech
Accelerating Data-Intensive Genome Analysis in the Cloud
Keynote: Applying theory to practice (and practice to theory)
Speaker: Ronald Fagin, IBM Research - Almaden
The speaker will talk about applying theory to practice, with a focus on two IBM case studies. In the first case study, the practitioner initiated the interaction. This interaction led to the following problem. Assume that there is a set of “voters” and a set of “candidates”, where each voter assigns a numerical score to each candidate. There is a scoring function (such as the mean or the median), and a consensus ranking is obtained by applying the scoring function to each candidate’s scores. The problem is to find the top k candidates, while minimizing the number of database accesses. The speaker will present an algorithm that is optimal in an extremely strong sense: not just in the worst case or the average case, but in every case! Even though the algorithm is only 10 lines long (!), the paper containing the algorithm won the 2014 Gödel Prize, the top prize for a paper in theoretical computer science.
The interaction in the second case study was initiated by theoreticians, who wanted to lay the foundations for “data exchange”, in which data is converted from one format to another. Although this problem may sound mundane, the issues that arise are fascinating, and this work made data exchange a new subfield, with special sessions in every major database conference.
This talk will be completely self-contained, and the speaker will derive morals from the case studies. The talk is aimed at both theoreticians and practitioners, to show them the mutual benefits of working together.