分会场赞助商v

1、英特尔高性能计算英特尔高性能计算 程从超程从超HPC Enables Innovation and Discoveries Source: Growing Challenges in HPC2“The Walls”System BottlenecksMemory | I/O | Storage Energy Efficient PerformanceSpace | Resiliency | Unoptimized SoftwareDivergent InfrastructureBarriers to Extending UsageResources Split Among Modeling a

2、nd Simulation | Big Data Analytics | Machine Learning | VisualizationHPCOptimizedDemocratization at Every Scale | Cloud Access | Exploration of New Parallel Programming ModelsBigDataHPCMachine LearningVisualizationA Holistic Architectural Approach is Required3ComputeMemoryFabricStorage PERFORMANCE I

3、 CAPABILITYTIMESystem SoftwareInnovative TechnologiesTighter IntegrationApplicationModernized CodeCommunityISVProprietarySystemMemoryCoresGraphicsFabricFPGAI/O3TFpeak DPPERFORMANCE3XFASTERST PERFORMANCEVS. KNC5XFASTERMCDRAM VS.DDR4 DIMMsKnights LandingIntel Xeon Phi processorCPUMCDRAMDDRNAND SSDHard

4、 Disk Drives61000XfasterTHAN NAND1000XenduranceOF NAND10XdenserTHAN DRAM3D XPoint TechnologyCPUDDRINTEL DIMMSIntel Optane SSDNAND SSDHard Disk Drives7Number of switch chips required, switch density, and fabric scalability are based on a full bisectional bandwidth (FBB) Fat-Tree configuration, using

5、a 48-port switch for Intel Omni-Path Architecture and 36-port switch ASIC for either Mellanox or Intel True Scale Fabric. *Other names and brands may be claimed as the property of others. 2.3X fabric scalability based on a 27,648-node cluster configured with the Intel Omni-Path Architecture using 48

6、-port switch ASICs, as compared with a 36-port switch chip that can support up to 11,664 nodes.8Omni-PatH Architecture100Gbit/sPERPORT1.3XHigherSWITCHDENSITY2.3XGreaterFABRICSCALABILITY6005004003002001000Switch Chips RequiredNodes364809121,3921,8722,3522,8323,312FEWER SWITCHESREQUIREDTighter System-

7、Level IntegrationInnovative Memory-Storage Hierarchy Keeps Data Closer to Compute *cache, memory or hybrid modeIntel Scalable System FrameworkCompute NodeProcessorMemory BusI/O NodeRemote StorageComputeTodayCachesLocal MemoryScratch StorageParallel File System (Hard Drive Storage)Higher Bandwidth. L

8、ower Latency and CapacityCompute Node storage moves to the memory busLocal memory is now faster & in processor packageComputeFutureCachesIntel DIMMs based on 3D XPoint TechnologyBurst Buffer Node withIntel Optane Technology SSDsParallel File System (Hard Drive Storage)On-Package High Bandwidth M

9、emory*SSD StorageIntel Optane Technology SSDsI/O Node storage moves to compute nodeSome remote data moves onto I/O nodeLocal MemoryMany workloads I One FrameworkModeling & SimulationMachineLearningHigh PerformanceData AnalyticsVisualizationIntel Xeon PhiTM ProcessorsIntel HPC OrchestratorIntel O

10、mni-Path FabricIntel Xeon ProcessorsIntel EthernetIntel Silicon Photonics Technology Intel Software ToolsIntel Cluster CheckerIntel Optane TechnologyIntel Solid State Drive Data CenterIntel Solutions for Lustre*Intel SSF system recommendationsIntel-Supported Software Defined VisualizationFabricCompu

11、teMemory/ StorageSoftwareModernize the code = UTILIZING the WHOLE DIE111 Core, No SIMD12 Cores, No SIMD12 Cores, 8x SIMD100X efficiencyIntel Parallel Studio XE 2017Technical, Enterprise & Cloud Compute Software Tools SuiteDesign, build, verify and tuneC+, C, Fortran, Python* and Java*Standards D

12、riven Parallel Models: OpenMP, MPI & TBBHighlights from 2017 editionFaster Python* application performance using Intel Distribution for Python and Intel VTune Amplifier XE.Faster deep learning on IA using Intel Math Kernel Library and Intel Data Analytics Acceleration LibraryQuickly assess appli

13、cation performance using snapshot features of VTune Amplifier XE and Intel Trace Analyzer and CollectorScale to next generation platforms including latest Intel Xeon Phi processor. Optimizations for Intel AVX-512, high bandwidth memory and explicit vectorization for compiler and analysis tools. Crea

14、te Faster CodeFaster.machines see(Object Localization and Image Classification ).we shop and are entertained(Recommendation Engines, Missing Interactions).we detect fraud and secure systems(Clustering and Outliers).we predict events(Temporal Data Mining).machines make decisions(Agents acting within Environments)Machine Learning | Changing How.we interact with our devices(Forecasting/prediction based on Sequences)Conv Neural Networks (CNNs)Support Vector MachinesRestricted Boltzmann (RBM)Alternating Lea