sponsored byACMIEEE The International Conference for High Performance 
Computing, Networking, Storage and Analysis
FacebookTwitterGoogle PlusLinkedInYouTubeFlickr

SCHEDULE: NOV 15-20, 2015

When viewing the Technical Program schedule, on the far righthand side is a column labeled "PLANNER." Use this planner to build your own schedule. Once you select an event and want to add it to your personal schedule, just click on the calendar icon of your choice (outlook calendar, ical calendar or google calendar) and that event will be stored there. As you select events in this manner, you will have your own schedule to guide you through the week.

libSkylark: A Framework for High-Performance Matrix Sketching for Statistical Computing

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Georgios Kollias, Yves Ineichen, Haim Avron, Vikas Sindhwani, Ken Clarkson, Costas Bekas, Alessandro Curioni

ROOM:Level 4 - Lobby


Matrix-based operations lie at the heart of many tasks in machine learning and statistics. Sketching the corresponding matrices is a way to compress them however preserving their key properties. This translates to dramatic reductions in execution time when the tasks are performed over the sketched matrices, while at the same time retaining provable bounds within practical approximation brackets. libSkylark is a high-performance framework enabling the sketching of potentially huge, distributed matrices and then applying the machinery of associated statistical computing flows. Sketching typically involves projections on randomized directions computed in parallel. libSkylark integrates state-of-the-art parallel pseudorandom number generators and their lazily computed streams with communication-minimization techniques for applying them on distributed matrix objects and then chaining the output into distributed numerical linear algebra and machine learning kernels. Scalability results for the sketching layer and example applications of our framework in natural language processing and speech recognition are presented.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Georgios Kollias - IBM Corporation

Yves Ineichen - IBM Corporation

Haim Avron - IBM Corporation

Vikas Sindhwani - Google

Ken Clarkson - IBM Corporation

Costas Bekas - IBM Corporation

Alessandro Curioni - IBM Corporation

Add to iCal  Click here to download .ics calendar file

Add to Outlook  Click here to download .vcs calendar file

Add to Google Calendarss  Click here to add event to your Google Calendar