ICPP 2014 Keynotes Summary09/24

Data Centric Systems: The Next Paradigm in ComputingSpeaker:  Dr. Tilak Agerwala

◦Vice President, Data Centric Systems◦IBM T.J. Watson Research Center

2014/09/09

Data-Centric SystemHPE (High Performance

Environment)◦= HPC + HPA

Mixed compute capabilities required.◦Heterogeneity is important.

IBM Data-Centric Design PrinciplesMinimize data motionEnable compute in all levels of

the systems hierarchyModularityApplication designLeverage OpenPOWER

High Performance Computing - Future DirectionsSpeaker:  Prof. Jack Dongarra

◦University of Tennessee, Knoxville

2014/09/10

Top500 Factoids There are 37 systems > Pflop /s (up 6 from November). About 90% of all the systems on the Top500 list are

integrated by U.S. vendors, including 65 of the 76 Chinese supercomputers.

HP has 182 systems on this list, or more than 36%, followed by IBM with 176, or 35%. Cray has 50 or 10%, SGI at 19 systems, and Dell at 8 systems.

Intel processors largest share, 87% followed by AMD, 6%.

For the first time, < 50% of Top500 are in the U.S. -- just 233 of the systems are U.S.-based, China #2 w/76.

IBM’s BlueGene/Q is still the most popular system in the TOP10 with four entries.

Infiniband found in 221 systems, GigE in 202, 10-GigE in 75.

Issue: Memory TransferCommunication bounded operation

◦Real performance < peak performance◦“Its all about data movement”◦Ex:

Take two double precision vectors x and y of size n=375,000.

Time to move the vectors from memory to cache: (6MBytes) / (25.6GBytes/sec) = 0.23ms

Time to perform computation of DOT: (2n flop) / (56Gflop/sec) = 0.01ms

eBay Storage: from Good to GreatSpeaker:  Farid Yavari

◦Sr. Storage Architect - Global Platform and Infrastructure (GPI)

◦eBay Inc.

2014/09/11

Elastic InfrastructureAn infrastructure that can spawn,

destroy, grow, shrink and move processes dynamically and efficiently within and across data centers. ◦Automated Control Plane◦Resource Pool◦Traffic Management

Key Initiatives to Enable an Elastic InfrastructureSeparation of Storage and

Compute ◦Hadoop use case

Software defined storage, software defined network

Cloud, SLA, OLA based services ◦Standardization ◦Automation ◦Show/Chargeback ◦Self Service