CINECA PRACE Tier-0 System Architecture: 10 BGQ Frame Model: IBM-BG/Q Processor Type: IBM PowerA2, 1.6 GHz Computing Cores: Computing Nodes: RAM: 1GByte / core Internal Network: 5D Torus Disk Space: 2PByte of scratch space Peak Performance: 2PFlop/s Available for ISCRA & PRACE call for projects
The PRACE RI provides access to distributed persistent pan-European world class HPC computing and data management resources and services. Expertise in efficient use of the resources is available through participating centers throughout Europe. Available resources are announced for each Call for Proposals.. Peer reviewed open access PRACE Projects (Tier-0) PRACE Preparatory (Tier-0) DECI Projects (Tier-1) European Local Tier 0 Tier 1 Tier 2 National
BG/Q I/O architecture BG/Q compute racks BG/Q IO SwitchFile system servers IB PCI_E IB IB SAN
I/O drawers I/O nodes PCIe 8 I/O nodes At least one I/O node for each partition/job Minimum partition/job size: 64 nodes, 1024 cores
PowerA2 chip, basic info 64bit RISC Processor Power instruction set (Power1…Power7, PowerPC) 4 Floating Point units per core & 4 way MT 16 cores (17th Processor core for system functions) 1.6GHz 32MByte cache system-on-a-chip design 16GByte of RAM at 1.33GHz Peak Perf gigaflops power draw of 55 watts 45 nanometer copper/SOI process (same as Power7) Water Cooled
9 PowerA2 FPU Each FPU on each core has four pipelines execute scalar floating point instructions four-wide SIMD instructions two-wide complex arithmetic SIMD inst. six-stage pipeline maximum of eight concurrent floating point operations per clock plus a load and a store.
EURORA #1 in The Green500 List June 2013 What EURORA stant for? EURopean many integrated cORe Architecture What is EURORA? Prototype Project Founded by PRACE 2IP EU project Grant agreement number: RI Co-designed by CINECA and EUROTECH Where is EURORA? EURORA is installed at CINECA When EURORA has been installed? March 2013 Who is using EURORA? All Italian and EU researchers through PRACE Prototype grant access program 3,200MFLOPS/W – 30KW
Why EURORA? (project objectives) Address Today HPC Constraints: Flops/Watt, Flops/m2, Flops/Dollar. Efficient Cooling Technology: hot water cooling (free cooling); measure power efficiency, evaluate (PUE & TCO). Improve Application Performances: at the same rate as in the past (~Moore’s Law); new programming models. Evaluate Hybrid (accelerated) Technology: Intel Xeon Phi; NVIDIA Kepler. Custom Interconnection Technology: 3D Torus network (FPGA); evaluation of accelerator-to- accelerator communications.
64 compute cards 128 Xeon SandyBridge (2.1GHz, 95W and 3.1GHz, 150W) 16GByte DDR3 1600MHz per node 160GByte SSD per node 1 FPGA (Altera Stratix V) per node IB QDR interconnect 3D Torus interconnect 128 Accelerator cards (NVIDA K20 and INTEL PHI) EURORA prototype configuration
Node card 13 Xeon PHI K20
Node Energy Efficiency 14 Decreases!
FERMI (IBM BGQ) PLX (IBM x86+GPU) Eurora (Eurotech hybrid) HPC Data store Workspace 3.6PByte Repository 1.8PByte Tape 1.5PB HPC Engines Network Custom FERMIEURORA IB EURORAPLXStoreNubes Gbe InfrastructureInternet Fibre Store External Data Sources Labs PRACE EUDATProjects Data Processing Workloads FERMI PLX viz High througput Big mem DB Data mover processing Web serv. FECNUBES Cloud serv. We b ArchiveFTP HPC Workloads PRACE ISCRA LISA LabsIndustry AgreementsProjects Training HPC Services HPC Cloud FECPLXStoreNubes #12 Top500 2PFlops peak cores 163Tbyte RAM Power 1.6GHz #1 Green PFlops peak 1024 x86 cores 64 Intel PHI 64 NVIDIA K20 0.3PFlops peak ~3500 x86 procs 548 NVIDIA GPU 20 NVIDIA Quadro 16 Fat nodes
CINECA services High Performance Computing Computational workflow Storage Data analytics Data preservation (long term) Data access (web/app) Remote Visualization HPC Training HPC Consulting HPC Hosting Monitoring and Metering … For academia and industry
Workspace 3.6PByte Core Data Processing viz Big mem DB Data moverprocessing We b serv. We b ArchiveFTP Core Data Store Repository 5PByte Tape 5+ PByte Internal data sources (data centric) Infrastructure (Q3 2014) Cloud service Scale-Out Data Processing FERMI X86 Cluster Laboratories PRACEEUDAT Other Data Sources External Data Sources Human Brain Prj SaaS APP Analytics APPParallel APP New analytics cluster New storage
Requisiti di alto livello del sistema Potenza elettrica assorbita: 400KW Dimensione fisica del sistema: 5 racks Potenza di picco del sistema (CPU+GPU): nell'ordine di 1PFlops Potenza di picco del sistema (solo CPU): nell'ordine di 300TFlops New Tier 1 CINECA Procurement Q3 2014
Requisiti di alto livello del sistema Architettura CPU: Intel Xeon Ivy Bridge Numero di core per CPU: >3GHz, oppure 2.4GHz La scelta della frequenza ed il numero di core dipende dal TDP del socket, dalla densità del sistema e dalla capacità di raffreddamento Numero di server: , ( Peak perf = 600 * 2socket * 12core * 3GHz * 8Flop/clk = 345TFlops ) Il numero di server del sistema potrà dipendere dal costo o dalla geometria della configurazione in termini di numero di nodi solo CPU e numero di nodi CPU+GPU Architettura GPU: Nvidia K40 Numero di GPU: >500 ( Peak perf = 700 * 1.43TFlops = 1PFlops ) Il numero di schede GPU del sistema potrà dipendere dal costo o dalla geometria della configurazione in termini di numero di nodi solo CPU e numero di nodi CPU+GPU Tier 1 CINECA
Requisiti di alto livello del sistema Vendor identificati: IBM, Eurotech DRAM Memory: 1GByte/core Verrà richiesta la possibilità di avere un sottoinsieme di nodi con una quantità di memoria più elevata Memoria non volatile locale: >500GByte SSD/HD a seconda del costo e dalla configurazione del sistema Cooling: sistema di raffreddamento a liquido con opzione di free cooling Spazio disco scratch: >300TByte (provided by CINECA) Tier 1 CINECA
Roadmap to Exascale (architectural trends)
HPC Architectures two model Hybrid: Server class processors: Server class nodes Special purpose nodes Accelerator devices: Nvidia Intel AMD FPGA Homogeneus: Server class node: Standar processors Special porpouse nodes Special purpose processors
Architectural trends Peak PerformanceMoore law FPU PerformanceDennard law Number of FPUs Moore + Dennard App. Parallelism Amdahl's law
Programming Models fundamental paradigm: Message passing Multi-threads Consolidated standard: MPI & OpenMP New task based programming model Special purpose for accelerators: CUDA Intel offload directives OpenACC, OpenCL, Ecc… NO consolidated standard Scripting: python
But! Si lattice 0.54 nm There will be still 4~6 cycles (or technology generations) left until we reach 11 ~ 5.5 nm technologies, at which we will reach downscaling limit, in some year between (H. Iwai, IWJT2008). 300 atoms! 14nm VLSI
Dennard scaling law (downscaling) L’ = L / 2 V’ = V / 2 F’ = F * 2 D’ = 1 / L 2 = 4D P’ = P do not hold anymore! The power crisis! L’ = L / 2 V’ = ~V F’ = ~F * 2 D’ = 1 / L 2 = 4 * D P’ = 4 * P Increase the number of cores to maintain the architectures evolution on the Moore’s law Programming crisis! The core frequency and performance do not grow following the Moore’s law any longer new VLSI gen. old VLSI gen.
The cost per chip “is going down more than the capital intensity is going up,” Smith said, suggesting Intel’s profit margins should not suffer because of heavy capital spending. “This is the economic beauty of Moore’s Law.” And Intel has a good handle on the next production shift, shrinking circuitry to 10 nanometers. Holt said the company has test chips running on that technology. “We are projecting similar kinds of improvements in cost out to 10 nanometers,” he said. So, despite the challenges, Holt could not be induced to say there’s any looming end to Moore’s Law, the invention race that has been a key driver of electronics innovation since first defined by Intel’s co-founder in the mid-1960s. Moore’s Law Economic and market law From WSJ Stacy Smith, Intel’s chief financial officer, later gave some more detail on the economic benefits of staying on the Moore’s Law race. It is all about the number of chips per Si wafer!
What about Applications? In a massively parallel context, an upper limit for the scalability of parallel applications is determined by the fraction of the overall execution time spent in non-scalable operations (Amdahl's law). maximum speedup tends to 1 / ( 1 − P ) P= parallel fraction core P = serial fraction=
HPC Architectures two model Hybrid, but… Homogeneus, but… What 100PFlops system we will see … my guess IBM (hybrid) Power8+Nvidia GPU Cray (homo/hybrid) with Intel only! Intel (hybrid) Xeon + MIC Arm (homo) only arm chip, but… Nvidia/Arm (hybrid) arm+Nvidia Fujitsu (homo) sparc high density low power China (homo/hybrid) with Intel only Room for AMD console chips
Chip Architecture Intel ARM NVIDIA Power AMD Strongly market driven Mobile, Tv set, Screens Video/Image processing New arch to compete with ARM Less Xeon, but PHI Main focus on low power mobile chip Qualcomm, Texas inst., Nvidia, ST, ecc new HPC market, server maket GPU alone will not last long ARM+GPU, Power+GPU Embedded market Power+GPU, only chance for HPC Console market Still some chance for HPC