Where to go from here? Get real experience building systems! Opportunities: 496 projects –More projects: 491r/571r class next term –Theme: Harnessing massively multicore systems (e.g., GPUs) – USRA funding for summer –(applications due January 2011)

A few projects … The top 1% of searchers performs a full 13% of all searches in a given month. If you extend this to the top 20% the number of queries increase to roughly 70%." Read more at: " Although these relations are not news, the argument that follows and the link with advertisement revenues are really interesting. The goal of this project is to understand which queries are more likely to lead to advertising revenue using the search engine data Microsoft has (partially) made available [traces from their Live Search engine] Predict advertised item popularity?

Platform Example – Argonne Blue Gene/P 160K cores 10 Gb/s Switch Complex GPFS 24 servers IO rate : 8GBps= 51KBps / core !! 2.5K IO Nodes Torus Network 2.5 GBps per node 3D Torus 850 MBps per 64 nodes Tree The central storage is a bottleneck There are underutilized resources close to application

MosaStore Evaluation Overall: 1.52x DOCK6 Workflow Stages Read input, compute, and write temporary results Summarize, sort, and select Archive Versatile Storage Optimizations Cache the input data Cache temporary files Asynch. flush results to GPFS Results (8K processors) 1.06x 11.76x 1.51x Zhang et. al., “Design and Evaluation of a Collective I/O Model for Loosely- coupled Petascale Programming”, MTAGS ’09.

A few NetSysLab  P2P data storage system (MosaStore)  Application-level GPU harnessing  Online social systems

StoreGPU GPUs dramatically change the computation cost landscape.  10x FLOPS, 10x Memory bandwidth,  yet same cost! Q: Does the 10x reduction in computation costs GPUs offer change the way we design/implement (distributed) storage system? System design: balancing act in a multi-dimensional space.

Data deduplication System -- Prototype Evaluation Checkpointing a BLAST application 100 times 76% improvement in write throughput No negative impact on concurrent applications Throughput (MB/s) [S. Al-Kiswany, A. Gharaibeh, S. Gopalakrishnan, and M. Ripeanu, “A GPU Accelerated Storage System”, Submitted to NSDI ‘10.] no-SD SD-CPU SD-GPU

Characterizing Online Social Systems CiteULike, Flickr, YouTube, Patterns of production/consumption of information are relatively unexplored Usage patterns inform system design –Recommendation –Content pre-fetching –Spam detection

Where to go from here? Get real experience building systems! Opportunities: 496 projects –More projects: 491r/571r class next term –Autonomic Systems – USRA funding for summer –(applications due January 2010)