SOSCIP Platforms. SOSCIP Platforms

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1 SOSCIP Platforms SOSCIP Platforms 1

2 SOSCIP HPC Platforms Blue Gene/Q Cloud Analytics Agile Large Memory System 2

3 SOSCIP Platforms Blue Gene/Q Platform 3

4 top500.org Rank Site System Cores Rmax (TFlop/s) Rpeak (TFlop/s) Power (kw) 1 National Super Computer Center in Guangzhou, China Tianhe-2 (MilkyWay-2)- TH-IVB-FEP Cluster, Intel Xeon E C 2.200GHz, TH Express-2, Intel Xeon Phi 31S1P, NUDT 2 DOE/SC/Oak Ridge National Laboratory, United States Titan- Cray XK7, Opteron C 2.200GHz, Cray Gemini interconnect, NVIDIA K20x, Cray Inc. 3 DOE/NNSA/LLNL, United States Sequoia- BlueGene/Q, Power BQC 16C 1.60 GHz, Custom, IBM RIKEN Advanced Institute for Computational Science (AICS), Japan K computer, SPARC64 VIIIfx 2.0GHz, Tofu interconnect, Fujitsu DOE/SC/Argonne National Laboratory, United States Mira- BlueGene/Q, Power BQC 16C 1.60GHz, Custom, IBM DOE/NNSA/LANL/SNL, United States Trinity- Cray XC40, Xeon E5-2698v3 16C 2.3GHz, Aries interconnect, Cray Inc Swiss National Supercomputing Centre (CSCS), Switzerland 8 HLRS - Höchstleistungsrechenzentrum Stuttgart, Germany 9 King Abdullah University of Science and Technology, Saudi Arabia 10 Texas Advanced Computing Center/Univ. of Texas, United States 4 Piz Daint- Cray XC30, Xeon E C 2.600GHz, Aries interconnect, NVIDIA K20x, Cray Inc. Hazel Hen- Cray XC40, Xeon E5-2680v3 12C 2.5GHz, Aries interconnect, Cray Inc. Shaheen II- Cray XC40, Xeon E5-2698v3 16C 2.3GHz, Aries interconnect, Cray Inc. Stampede- PowerEdge C8220, Xeon E C 2.700GHz, Infiniband FDR, Intel Xeon Phi SE10P, Dell

3 DOE/NNSA/LLNL, United States Sequoia- BlueGene/Q, Power BQC 16C 1.60 GHz, Custom, IBM 3120000 33862.7 54902.4 17808 560640 17590 27112.5 8209 1572864 17173.2 20132.

5 SOSCIP's Blue Gene/Q Specifications 64k core cluster 1.6 GHz cores 64 TB RAM 5-D torus interconnect Performance Measured: 716 Tflops Efficiency: 2.1 Gflops/W 5

6 Typical Applications Disaster planning and mitigation Molecular modeling Protein folding Drug discovery Computational fluid dynamics Nuclear fusion Genomics Brain modeling Climate and weather 6 Complex infrastructure simulation

Computational fluid dynamics Nuclear fusion Genomics

7 Suitable Applications Large-scale, massively parallel and distributed 1,024 cores or more Need low-latency, high-bandwidth communication for ( ) { } Written in C/C++, Fortran, or Python // // GPL // // // Me 2015 // MPI_Init(...)... Sendrecv(... )... Custom and open-source software Use MPI and OpenMP 7

Written in C/C++, Fortran, or Python // // GPL // // // Me 2015 //

8 Ocean Mixing Simulation H. Salehipour & W.R. Peltier (University of Toronto) 8

9 SOSCIP Platforms Cloud Analytics Platform 9

10 Big Data Challenges Three V's: Volume, Velocity, Variety Processing power Storage and data locality Analytics frameworks 10

11 Cloud Platform Hardware ps- & hs-series blades POWER servers Powerful x86 and IBM Power Systems servers GPFS storage system (1 petabyte) Infiniband and 10 GbE networks x86 NeXtScale x86 idataplex 11

12 Software Available IBM InfoSphere Streams ILOG CPLEX IBM InfoSphere BigInsights 12 Plus almost anything from the IBM Academic Initiative catalog!

13 Typical Applications Real-time Medical Data Collection and Analytics Text Analytics Cybersecurity Document Filtering Energy Systems Data Analytics Image Processing Machine Learning Social Media Analytics Medical Records Analytics 13

Filtering Energy Systems Data Analytics Image Processing

14 Suitable Applications Require IBM analytics software Require other commercial software Small number of cores (< 100) Small clusters Big data storage (1-100 TB) 14

application Detected edges highly correlate with known mineral deposits IBM

15 Analyzing Geospatial Patterns Neil Banerjee (Western University) Multidirectional edge detection algorithm for mineral exploration and mining application Detected edges highly correlate with known mineral deposits IBM BigInsights for high level of automation 16 Know Mineral Locations Hill shading Feature detection

16 SOSCIP Platforms Large Memory System 17

17 Big Data and Memory CPU ~5-8 GB/s Latency: > 1 ms Disk Storage Pros - Inexpensive - Large Capacity Cons -Slow! GB/s Latency: < 100 ns System Memory (DRAM) Pros - Fast! Cons - Cost per GB - Limited capacity per server 18

50-200 GB/s Latency: < 100 ns System Memory (DRAM) Pros

18 LMS Specification 3-nodes acting as single system 64 x86 cores vsmp 4.5 TB RAM (1.5 TB per node) Shared memory programming 19

19 Suitable Applications Need to keep all data in RAM for speed Generate large amount of intermediate data Need > 128 GB of RAM Require medium number of cores (< 64) Need shared memory programming paradigm Use commercial software 20

128 GB of RAM Require medium number of cores (< 64) Need

20 SOSCIP Platforms Agile Computing Platform 21

21 The Case for FPGAs CPU Scaling ICT Power Consumption 22 FPGA acceleration offers: Algorithms in re-configurable circuitry High performance parallelism High power efficiency

22 Agile Computing Platform Development Environment Runtime Environment Fast x86 servers Simulate, debug, build Tools x86 and POWER8 Stratix V FPGAs 10 GbE FPGA Network on POWER8 23 Development Kit

23 Suitable Applications Real-time or time-critical processing Compute intensive Exploit parallelism in depth and/or width Wide vectorization Big data Non-traditional data types 24

24 Typical Applications Health/Medical systems Image/Video Processing Machine Learning Signal Processing Data Security 25 Big Data Analytics

implements superior motion correction algorithm IBM InfoSphere Streams on POWER constructs graphs of brain

25 Real-time fmri Brain Analytics Mark Daley (Western University) The problem: brain activity scans take days to analyze The solution: an FPGA-accelerated real-time analytics engine FPGA replaces 48 x86 cores and implements superior motion correction algorithm IBM InfoSphere Streams on POWER constructs graphs of brain networks 40x faster than single process on x86 26 Graph updates every s Results in seconds instead of days!

26 When am I ready for SOSCIP? Scaling computing power Scaling storage size Unique technology needs Software needs 28

27 Platform Summary Platform CPU Operating Systems Commercial Software Languages Support Blue Gene/Q PowerPC Linux No C, C++, Python, Fortran SciNet, IBM specialist Large Memory System x86 Linux Yes All HPCVL Cloud Analytics x86, POWER Linux, Windows, AIX Yes All SHARCNET, IBM specialist Agile x86, POWER8 Linux Yes All + OpenCL, Verilog/VHDL SHARCNET, IBM specialist 29

28 Notices: POWER8, Power Systems logo, InfoSphere, InfoSphere Streams logo, InfoSphere BigInsights, InfoSphere BigInsights logo, SPSS, Cognos, Rational, and the IBM logo are trademarks or registered trademarks of International Business Machines Corp. Altera, Quartus II and Stratix are trademarks of Altera Corp. ModelSim is a trademark of Mentor Graphics OpenCL and the OpenCL logo are trademarks of Apple Inc. Matlab and the Matlab logo are trademarks of Mathworks Inc. Python and the Python logo are trademarks of the Python Software Foundation Nallatech is a trademark of Interconnect Systems Inc. 30

29 31 backup

30 What are FPGAs? FPGA = Field Programmable Gate Array Multiply Configure groups of logic elements to construct function blocks FPGA chips have 100,000's of configurable logic circuits Load Add Add Store Connect several blocks into data pipeline Logic Element Simultaneously work on different elements of a data stream at each stage on every clock cycle 32 Interconnect Onboard RAM

$*restrict z) { int index = get_global_id(0); } Kernel code z[index] = x[index] + y[index]; OpenCL$

31 OpenCL TM attribute ((num_simd_work_items(4))) attribute ((reqd_work_group_size(64,1,1))) attribute ((num_compute_units(2))) kernel void vectoradd( global const int *x, global const int *y, global int *restrict z) { int index = get_global_id(0); } Kernel code z[index] = x[index] + y[index]; OpenCL Compiler Programming file Host code clenqueuendrangekernel(queue, kernel, dim, offset, size, local_size,...); 33

Address Space HW Managed Cache Coherence RAM CAPP:

32 CAPI Coherent Accelerator Processor Interface SMP links PSL PCIe CAPP CAPI Protocol Features Shared Virtual Address Space HW Managed Cache Coherence RAM CAPP: Coherent Attached Processor Proxy, PSL: POWER Service Layer 34

Parallel Computing. Introduction

Parallel Computing. Introduction Parallel Computing Introduction Thorsten Grahs, 14. April 2014 Administration Lecturer Dr. Thorsten Grahs (that s me) t.grahs@tu-bs.de Institute of Scientific Computing Room RZ 120 Lecture Monday 11:30-13:00