SCAI SuperComputing Application & Innovation. Sanzio Bassini October 2017
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1 SCAI SuperComputing Application & Innovation Sanzio Bassini October 2017
2 The Consortium Private non for Profit Organization Founded in 1969 by Ministry of Public Education now under the control of Ministry for Education, University and Research Consortium structure 70 Universities Ministry of University and research CNR, INFN, INAF, OGS, SZN CREA, INDIRE, INVALSI Main premises in Bologna, point of presence in Milan and Rome Oct, 2017 Cineca SuperComputing Applications & Innovations 2
3 Current Computing systems Oct, 2017 Cineca SuperComputing Applications & Innovations 3
4 Computing systems next upgrade Oct, 2017 Cineca SuperComputing Applications & Innovations 4
5 Success stories Oct, 2017 Cineca SuperComputing Applications & Innovations 5
6 Main European funded R&D HPC infrastructure Digital infrastructure Center of Excellence Material Science Fabric of the future Industry 4.0 Environment Life Science Multimedia / Cultural Heritage Energy efficiency Oct, 2017 Cineca SuperComputing Applications & Innovations 6
7 Collaborations with qualified national players Eni E&P Research HPC system management Production management Applications development Innovation technology Protezione Civile / SMR Regione Emilia Romagna Operation numerical weather forecast Data Post processing ARPA Piemonte Environmental numerical forecast Unipol Risk management Big data ISTAT Web crawlers Predictive analysis Telethon Integrated peer review Repository genomic data Human Technopole Oct, 2017 Cineca SuperComputing Applications & Innovations 7
8 Technology innovation: proof of concept Manufacturing Engineering Service Oct, 2017 Cineca SuperComputing Applications & Innovations 8
9 Middleware and Data Management Deep Learning: Working with LENOVO and NVIDIA to benchmark and support DL common libraries on INTEL KNL and NVIDIA K80 & P100 architectures AI Lab in collaboration with IBM & LENOVO Cloud Computing Recondition of MARCONI A1 partition from OPA to Eth ROCE to enhance the Cloud Computing Platform from 25 hypervisors on PICO to approximately 200 on Marconi (September 2017) UNIPOL R&D Research Lab on Machine and Deep Learning topics Long term (3 years) framework agreement Telethon/SIGU Data Repository Repository of genomic data and metadata EVAR Planner Collaboration with Humanitas Ospedale Milano for the development of an application for Endovascular stent graft configuration Collaboration with Microsoft & Amazon for testing Azure and AWS services Oct, 2017 Cineca SuperComputing Applications & Innovations 9
10 (New) Cineca HPC infrastructure GSS OPA Marconi A3 OPA Marconi A2 OPA Marconi A1 OPA Tier0 GSS Login Gateway New Core Network NewG SS New ETHcore NAT Servers Internet HBP PRACE/EUDAT CNAF 3 4 new Capacity: 11 14PB MellanoxGW tape Fibre SW New ETH 100Gbit New GBit ETH SW Upgrade, 4 new driver 10Tbyte tapes: 30PByte Max capacity servers Mellanox FDR NFS Servers FEC Servers Ex PICO 5100 TMS Marconi A1 ETH Tier1 Oct, 2017 Cineca SuperComputing Applications & Innovations 10
11 Systems evolution (HPC Cloud) Logical Name Model Architecture Processor HPC Cloud (September 2017) NeXtScale Server Intel Broadwell Intel E v4 Broadwell GHz # of core 18 # of node ~ 400 Ensure high performance on single node Different workloads supported Interactive Computer Isolated environment for highsecurity environments # of rack - RAM per node 128 GB Interconnection Mellanox ETH 10/100 with ROCE Operating System GNU/Linux Total Power - Peak Performance - Oct, 2017 Cineca SuperComputing Applications & Innovations 11
12 Systems evolution (Data Processing) Logical Name Model Architecture Processor D.A.V.I.D.E. (August 2017) E4 Cluster Open Rack OpenPower NVIDIA NVLink OpenPower 8 NVIDIA Tesla P100 SXM2 # of core - # of node 45 x (2 Power8 + 4 Tesla P100) # of rack - RAM per node - Interconnectio n Operating System Mellanox EDR GNU/Linux Result of a PCP (Pre Commercial Procurement) commissioned by PRACE Based on OpenPOWER architecture, using IBM POWER8 processors with NVLink bus and the ultra performing GPGPU NVIDIA TESLA P100 SXM2. Low power consumption Total Power - Peak Performance ~ 1 Pflops Oct, 2017 Cineca SuperComputing Applications & Innovations 12
13 The Roadmap Logical Name Tier 0 - FERMI (June 2012) Tier 1 - GALILEO (December 2014) Big data - PICO (October 2014) Peak Performance ~ 2 Pflops; ~ 5 PByte ~ 0,5 PFlops ~ 0,3 Pflops; ~ 15 Pbyte Logical Name MARCONI T0 (2016 / 2017) MARCONI T1 (2017) Peak Performance ~ 20 Pflops; ~ 15 Pbyte ~1 Pflops; ~ 20 Pbyte Logical Name Tier 0 pre exascale ( ) Peak Performance ~ 50 Pflops; ~ 50 Pbyte on line storage; ~ 50 Pbyte repository Logical Name Tier 0 pre exascale ( ) Peak Performance ~ 250 Pflops; ~ 50 Pbyte on line storage; ~ 50 Pbyte repository Oct, 2017 Cineca SuperComputing Applications & Innovations 13
14 On going project towards Digital single market Integration of CINECA HPC and INFN HTC computing infrastructure to provide services to: Institutional basic and applied research Enabling for Public administrations Proof of concept and innovation for private organizations and industries DATA Network Integrated Research Data Infrastructure Oct, 2017 Cineca SuperComputing Applications & Innovations 14
15 Implementation model Research Services Layer Private & Public Sectors HPC & BIG Data DATA Infrastructure HTC & BIG Data Network & transport layer Oct, 2017 Cineca SuperComputing Applications & Innovations 15
16 Data analytics software Apache Hadoop/Spark related applications (a, b) Spark (Python, Scala and R shells) Spark libraries (MLlib, Graphx, SQL, streaming) Python Machine Learning libraries (Sci Kit, Pylearn2, ) R (SparkR, BigR) H2O Mahout Interfaces/Notebooks (a,b) Jupyter/all spark notebook (Scala, R, Python) Spark Notebook Apache Zeppelin Beaker Notebook R Studio server High Performance tools Google Tensorflow Intel DAAL Data Analytics Acceleration Library Oct, 2017 Cineca SuperComputing Applications & Innovations 16
17 Model size is important: Deep Learning at HPC winning model of ILSVRC2012 classification task AlexNet: 5 conv. Layers + 3 fullyconn. layers Data size scaling Larger datasets usually improve accuracy High Arithmetic intensity: Multiple inputs, multiple outputs, batch: GEMM Dedicated facilities data storage parallel computation hardware (manycores, GPUs) Optimized primitives Oct, 2017 Cineca SuperComputing Applications & Innovations 17
18 Tested software frameworks Community based you can clone/fork them from github Common approach: NN is built by defining it s computational graph High level language is preferred (Python, protobuf, LUA) Intra-node multi-gpu parallelization Hardware-independent layer Optimized backend engines: MKL and MKL-DNN for Intel based CPUs and many-cores cublas and cudnn for Nvidia GPUs Inter-node scaling: inital support: Tensorflow (grpc), Caffe (MPI) dedicated communication library (Intel MLSL) Oct, 2017 Cineca SuperComputing Applications & Innovations 18
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