Big Data in HPC. John Shalf Lawrence Berkeley National Laboratory

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1 Big Data in HPC John Shalf Lawrence Berkeley National Laboratory 1

mathematical models of nature Experiment:

theories involving complex phenomena that

experiments Enable comprehension of complex

down to understandable information There is

2 Evolving Role of Supercomputing Centers Traditional Pillars of science Theory: mathematical models of nature Experiment: empirical data about nature Scientific computing connects theory to experiment Computational models are used to test theories involving complex phenomena that cannot be matched directly against experiments Enable comprehension of complex experimental data by distilling complex data down to understandable information There is no scientific discovery without a theory about underlying mechanism There is no scientific discovery without experimental validation of model Theory Simulation Mechanism Experiment Mathematical Models of Nature Evidence Confirm/Violate Analysis Empirical Data about Nature

3 Breakthrough science will occur at the interface of observation and simulation Environment and Climate Cosmology Materials and Chemistry Combustion Subsurface science New research challenges to make these work together -3-

Data Intensive Arch for 2017 (as imagined in 2012) Compute On-Package DRAM Capacity Memory On-node-Storage In-Rack Storage Interconnect Global Shared Disk Off-System Network Compute Intensive Arch n.

4 Data Intensive Arch for 2017 (as imagined in 2012) Compute On-Package DRAM Capacity Memory On-node-Storage In-Rack Storage Interconnect Global Shared Disk Off-System Network Compute Intensive Arch n.a. 5TF/socket 1-2 sockets GB HMC or Stack - 4-1TB/s 200TB/s.5-1TB CDIMM (opt.) Organized for Burst Buffer Spatially-oriented e.g. 3D-5D Torus ~1% nodes for ~1% nodes for IP Gateways 100TB/s 50GB/s/node 10TB/s/rack 10TB/s bisect 0.5TB/s aggregate 4GB/s per node Data Intensive Arch /! (root)! Dataset0! time =0.2345! validity =None! Dataset1! type, space! author =JoeBlow! Dataset0.1! subgrp! Dataset0.2! + + 5TF/sock 8+ sockets 1-4 TB Aggregate Chained-HMC 5-10TB Memory Class NVRAM TB SSD Cache or local FS 1-10PB Dist.Obj. DB (e.g whamcloud) All-to-All oriented e.g. Dragonfly or 3T ~10-20% nodes for 40GBe Ethernet to Direct from each node

Organized for Burst Buffer Maximizes bandwidth density near compute Spatially-oriented e.g. 3D-5D Torus ~1% nodes for ~1% nodes for IP Gateways 100TB/s 50GB/s/node 10TB/s/rack 10TB/s bisect 0.

5 Data Intensive Arch for 2017 (as imagined in 2012) Compute On-Package DRAM Capacity Memory On-node-Storage In-Rack Storage Interconnect Global Shared Disk Off-System Network Compute Intensive Arch 5TF/socket 1-2 sockets GB HMC or Stack - 5-1TB/s 200TB/s.5-1TB CDIMM (opt.) Goal: Maximum Computational Density and local n.a. bandwidth for given power/cost constraint. Organized for Burst Buffer Maximizes bandwidth density near compute Spatially-oriented e.g. 3D-5D Torus ~1% nodes for ~1% nodes for IP Gateways 100TB/s 50GB/s/node 10TB/s/rack 10TB/s bisect 0.5TB/s aggregate 4GB/s per node Data Intensive Arch 5TF/sock sockets Goal: Maximum Data Capacity and 1-4 TB global Aggregate Chained-HMC bandwidth for given power/cost 5-10TB constraint. Memory Class NVRAM Bring more TB storage SSD capacity near Cache compute or local (or FS conversely embed more /! author =JoeBlow! (root)! compute into the storage). Dataset0! Dataset1! subgrp! type, space! time =0.2345! validity =None! Dataset0.1! Dataset0.2! 1-10PB Dist.Obj. DB (e.g whamcloud) All-to-All oriented e.g. Dragonfly or 3T Requires software and programming environment support for such a paradigm shift ~10-20% nodes for 40GBe Ethernet to Direct from each node

6 Old/New Conception of Cloud/Datacenters (Simplified Conceptual Model of Interconnect Convergence) Old Conception Designed for externally facing TCP/IP Nearly 100% Std. TCP/IP ethernet inside and out The Datacenter/Cloud DMZ Router 90+% traffic Backbone 6

Conception Need Designed to Handle for Internal externally

for TCP/IP 80+% ethernet internal traffic inside and out

Router 90+% traffic Backbone High-Performance Fluid Center

7 Old/New Conception of Cloud/Datacenters (Simplified Conceptual Model of Interconnect Convergence) New Old Conception Need Designed to Handle for Internal externally Data facing Mining/Processing TCP/IP Nearly 100% Design Std. for TCP/IP 80+% ethernet internal traffic inside and out Cloud/Datacenter The Datacenter/Cloud 80+% of traffic DMZ Router 90+% traffic Backbone High-Performance Fluid Center Low Overhead, High Bandwidth, Semi-custom internal interconnect Crunchy TCP/IP Exterior 7

Short Talk: System abstractions to facilitate data movement in supercomputers with deep memory and interconnect hierarchy

Short Talk: System abstractions to facilitate data movement in supercomputers with deep memory and interconnect hierarchy François Tessier, Venkatram Vishwanath Argonne National Laboratory, USA July 19,