E4-ARKA: ARM64+GPU+IB is Now Here Piero Altoè. ARM64 and GPGPU
|
|
|
- Colleen Townsend
- 6 years ago
- Views:
Transcription
1 E4-ARKA: ARM64+GPU+IB is Now Here Piero Altoè ARM64 and GPGPU 1
2 E4 Computer Engineering Company E4 Computer Engineering S.p.A. specializes in the manufacturing of high performance IT systems of medium and high range. Our products aim to accomplish both industrial and scientific research requirements and space from universities to computing centers. Thanks to the established experience and quality in this circle, E4 has become a valued technology s supplier and it is acknowledged and appreciated by famous and prestigious organizations. ARM64 and GPGPU 2
3 The first ARM+GPU cuda based platform 2011 Features CPU GPU Memory Peak Performance Network Storage ARKA Blade NVIDIA Tegra 3 ARM Cortex A9 Quad-Core NVIDIA Quadro 1000M with 96 CUDA Cores 2GB x CPU 2GB x GPU 270 Single Precision GFLOPS 1x Gigabit Ethernet 1x SATA 2.0 Connector CARMA Devkit developed by SECO USB 3x USB 2.0 Display 3x HDMI + serial console available ARM64 and GPGPU 3
4 ARKA CLUSTER SHOC BENCH 2011 The Scalable Heterogeneous Computing Benchmark Suite (SHOC) is a collection of benchmark programs testing performance and stability (using CUDA and/or opencl) Test ARKA Intel+M2075 Units ARKA/M2075 Maxspflops GFlops 26% fft_sp GFlops 14% sgemm_n GFlops 20% dgemm_n GFlops 7% md_sp_bw GB/s 22% Reduction GB/s 25% Sort GB/s 19% triad_bw GB/s 7% ARM64 and GPGPU 4
5 How we started 2012 ARM64 and GPGPU 5
6 The first server ARM+K20 EK ARKA EK002 diagram Q7 Tegra 3 SOM PCIe 4x Gen 1 PLX PCIe switch PCIe PCIe PCIe Mini-itx carrier board PCIe Nvidia K20/K2000 ETH 1Gb 1x SATA SATA 2.5 SSD Node maximum power consumption 250W 1,170 Tflops Rpeak 4.5+ Gflops/W ARM64 and GPGPU 6
7 EK002 Efficiency 8x More efficient 5x More efficient ARM64 and GPGPU 7
8 Pedraforca Cluster rack 78 compute nodes 2 login nodes 4 36-port InfiniBand switches (MPI) 2 50-port GbE switches (storage) ARM64 and GPGPU 8
9 The X-gene1 SoC 8 Brawny Custom Cores X-Gene: Highly Integrated Server on a Chip TM 64b 2.4GHz 64b 2.4GHz 64b 2.4GHz 64b 2.4GHz 64b 2.4GHz ON-CHIP FABRIC 64b 2.4GHz 64b 2.4GHz 64b 2.4GHz High Speed Memory Ctrlr Broad, Fast Memory Network Accel SDN Etc. 4 x 10Gb Ethernet 16 x PCIe SATA PHYSICAL LAYER High-Speed Mixed-Signal 9 I/O ARM64 and GPGPU 9
10 RK003 PROTOTYPE PCIe x4 Mini-itx APM Mustang dev board PCIe 8x Gen 3 PLX PCIe switch PCIe x4 PCIe x4 PCIe x4 Nvidia K20 ETH 10Gb 1x SATA SATA 2.5 SSD Node maximum power consumption 250W 1,17 Tflops Rpeak 4.68 Gflops/W ARM64 and GPGPU 10
11 RK003 PROTOTYPE bandwidthtest]#./bandwidthtest [CUDA Bandwidth Test] - Starting... Running on... Device 0: Tesla K20m Quick Mode Host to Device Bandwidth, 1 Device(s) Mini-itx APM PCIe 8x PINNED Memory Transfers Mustang dev board Transfer Size (Bytes) Bandwidth(MB/s) Gen PLX PCIe switch PCIe PCIe PCIe Device to Host Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) ETH 10Gb 1x SATA Device to Device Bandwidth, 1 Device(s) PINNED Memory Transfers Transfer Size (Bytes) Bandwidth(MB/s) Result = PASS SATA 2.5 SSD PCIe x4 Nvidia K20 ARM64 and GPGPU 11
12 RK003 PROTOTYPE Ibv_rc_pingpong : Mbit/s 3.69 µs Mini-itx APM Mustang dev board PCIe 8x Gen 3 PLX PCIe switch PCIe PCIe PCIe x4 PCIe x4 Connect-X3 Nvidia K20 ETH 10Gb 1x SATA SATA 2.5 SSD ARM64 and GPGPU 12
13 RK003 PROTOTYPE PCIe Mini-itx APM Mustang dev board PCIe 8x Gen 3 PLX PCIe switch PCIe PCIe PCIe Nvidia K20 ETH 10Gb 1x SATA SATA 2.5 SSD Node maximum power consumption 250W 1,17 Tflops Rpeak 4.68 Gflops/W ARM64 and GPGPU 13
14 The ARKA RK003 Server (1) FEATURES Form Factor: 2U CPU 1x APM X-Gene 8 cores GPU up to 2x NVIDIA Kepler K20, K40, K80 Memory Up to 128 GB RAM DDR3 Network 2x 10 GbE SFP+, 1x Infiniband FDR QSFP Storage 4x SATA 3.0 Expansion slots 2x PCI-E 3.0 x8 (in x16) 1U motherboard X- gene 1 PCIe 8x Gen 3 PCIe 8x Gen 3 Mellanox ConnectX Nvidia K20/K40 2x ETH 10Gb 4 x SATA SATA 2.5 SSD ARM64 and GPGPU 14
15 The ARKA RK003 Server (2) OS: Ubuntu derivative for ARM LTS DEVELOPMENT TOOL: NVIDA CUDA 6.5 (compilers, libraries, SDK) MPI 2.0 Libraries GNU compilers Scalable Heterogeneous Computing (SHOC) Benchmark Suite CLUSTER, MONITORING, MANAGEMENT TOOLS (optional): Resource/Queue manager Monitoring tools for cluster Parallel shell for cluster-wide commands Bare-metal restore ARM64 and GPGPU 15
16 The ARKA RK003 Server (3) ARM64 and GPGPU 16
17 The ARKA RK003 GPU Performance Device 0: 'Tesla K20m' Device selection not specified: defaulting to device #0. Using size class: Starting Benchmarks --- Running benchmark BusSpeedDownload result for bspeed_download: Running benchmark BusSpeedReadback result for bspeed_readback: Running benchmark MaxFlops result for maxspflops: result for maxdpflops: Running benchmark DeviceMemory result for gmem_readbw: result for gmem_writebw: GB/sec GB/sec GFLOPS GFLOPS GB/s GB/s ARM64 and GPGPU 17
18 The ARKA RK003 GPU Performance Device 0: 'Tesla K20m' Device selection not specified: defaulting to device #0. Using size class: Starting Benchmarks --- Running benchmark BusSpeedDownload result for bspeed_download: Running benchmark BusSpeedReadback result for bspeed_readback: Running benchmark MaxFlops result for maxspflops: result for maxdpflops: Running benchmark DeviceMemory result for gmem_readbw: result for gmem_writebw: GB/sec GB/sec GFLOPS GFLOPS GB/s GB/s ARM64 and GPGPU 18
19 Infiniband ping pong test Ibv_rc_pingpong: Latency 1.57 usec Bandwidth 39.2Gb/s ARM64 and GPGPU 19
20 Time to solution (s) LAMMPS Benchmark LAMMPS Benchmark 1x RK cores X86 SETUP COMPILER -Intel compilers on E5-2650v2 -MKL -GCC on ARM -FFTW HARDWARE -Dual E5-2650v2, Infiniband QDR -ARM X-gene1+ 1 GPU Nvidia K20m INPUT FILES -WdV 1M particle ARM64 and GPGPU 20
21 Time to solution (s) LAMMPS Benchmark (2) SETUP COMPILER GNU Cuda 6.5 Cublas and cufft HARDWARE -Dual E5-2630v3 + 1 GPU Nvidia K20m -ARM X-gene1+ 1 GPU Nvidia K20m 1x RK003 1x E5-2630v3 + K20 INPUT FILES -WdV 1M particle steps ARM64 and GPGPU 21
22 Power Consumption SETUP COMPILER GNU Cuda 6.5 Cublas and cufft HARDWARE -Dual E5-2630v3 + 1 GPU Nvidia K20m -ARM X-gene1+ 1 GPU Nvidia K20m GPU performances: RK003 Xeon E5 - avg load power consumption: 229w MaxFlops: 1165 GFLOPS Benchmark: 5,1 GFLOP/w power consumption in idle: 92 W avg load power consumption: 410w MaxFlops: 1165 GFLOPS Benchmark: 2,8 GFLOP/w power consumption in idle: 151 ARM64 and GPGPU 22
23 CAVIUM SoC Up to 48 custom ARMv8 2.5GHz Single & Dual socket configs Integrated PCIe, SATA, 10/40GbE, Ethernet Fabric VirtSOC : Low latency end to end virtualization from VM to virtual port Four Workload Optimized Families Compute, Networking, Storage and Security Shipping today in Single and Dual Socket Configurations ARM64 and GPGPU 23
24 The ARKA RK004 Server FEATURES Form Factor: 2U CPU 1x CAVIUM Thunder-X 48 cores GPU up to 1x NVIDIA Kepler K20, K40, K80 Memory Up to 128 GB RAM DDR3 Network 2x 10 GbE SFP+, 1x IB FDR QSFP, 1x 40 GbE QSFP Storage 4x SATA 3.0 Expansion slots 1x PCI-E 3.0 x8 (in x16), 1x PCI-E 3.0 x8 BOOTH 430 ARM64 and GPGPU 24
25 Acknowledgment Cosimo Gainfreda Simone Tinti Wissam Abu-Ahmmad Francesca Tartaglione (now at OIST) Marco Ciacala Alessandro De Filippo Filippo Mantovavi ARM64 and GPGPU 25
26 BOOTH 430 ARM64 and GPGPU 26
Pedraforca: a First ARM + GPU Cluster for HPC
www.bsc.es Pedraforca: a First ARM + GPU Cluster for HPC Nikola Puzovic, Alex Ramirez We ve hit the power wall ALL computers are limited by power consumption Energy-efficient approaches Multi-core Fujitsu
CUDA on ARM Update. Developing Accelerated Applications on ARM. Bas Aarts and Donald Becker
CUDA on ARM Update Developing Accelerated Applications on ARM Bas Aarts and Donald Becker CUDA on ARM: a forward-looking development platform for high performance, energy efficient hybrid computing It
A176 Cyclone. GPGPU Fanless Small FF RediBuilt Supercomputer. IT and Instrumentation for industry. Aitech I/O
The A176 Cyclone is the smallest and most powerful Rugged-GPGPU, ideally suited for distributed systems. Its 256 CUDA cores reach 1 TFLOPS, and it consumes less than 17W at full load (8-10W at typical
CUDA on ARM Update. Developing Accelerated Applications on ARM. Bas Aarts and Donald Becker
CUDA on ARM Update Developing Accelerated Applications on ARM Bas Aarts and Donald Becker CUDA on ARM: a forward-looking development platform for high performance, energy efficient hybrid computing It
LAMMPS-KOKKOS Performance Benchmark and Profiling. September 2015
LAMMPS-KOKKOS Performance Benchmark and Profiling September 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox, NVIDIA
Increasing the efficiency of your GPU-enabled cluster with rcuda. Federico Silla Technical University of Valencia Spain
Increasing the efficiency of your -enabled cluster with rcuda Federico Silla Technical University of Valencia Spain Outline Why remote virtualization? How does rcuda work? The performance of the rcuda
OCTOPUS Performance Benchmark and Profiling. June 2015
OCTOPUS Performance Benchmark and Profiling June 2015 2 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the
MegaGauss (MGs) Cluster Design Overview
MegaGauss (MGs) Cluster Design Overview NVIDIA Tesla (Fermi) S2070 Modules Based Solution Version 6 (Apr 27, 2010) Alexander S. Zaytsev p. 1 of 15: "Title" Front view: planar
The rcuda middleware and applications
The rcuda middleware and applications Will my application work with rcuda? rcuda currently provides binary compatibility with CUDA 5.0, virtualizing the entire Runtime API except for the graphics functions,
7 DAYS AND 8 NIGHTS WITH THE CARMA DEV KIT
7 DAYS AND 8 NIGHTS WITH THE CARMA DEV KIT Draft Printed for SECO Murex S.A.S 2012 all rights reserved Murex Analytics Only global vendor of trading, risk management and processing systems focusing also
SNAP Performance Benchmark and Profiling. April 2014
SNAP Performance Benchmark and Profiling April 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: HP, Mellanox For more information on the supporting
Exploiting CUDA Dynamic Parallelism for low power ARM based prototypes
www.bsc.es Exploiting CUDA Dynamic Parallelism for low power ARM based prototypes Vishal Mehta Engineer, Barcelona Supercomputing Center vishal.mehta@bsc.es BSC/UPC CUDA Centre of Excellence (CCOE) Training
HP GTC Presentation May 2012
HP GTC Presentation May 2012 Today s Agenda: HP s Purpose-Built SL Server Line Desktop GPU Computing Revolution with HP s Z Workstations Hyperscale the new frontier for HPC New HPC customer requirements
CPMD Performance Benchmark and Profiling. February 2014
CPMD Performance Benchmark and Profiling February 2014 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the supporting
The Mont-Blanc Project
http://www.montblanc-project.eu The Mont-Blanc Project Daniele Tafani Leibniz Supercomputing Centre 1 Ter@tec Forum 26 th June 2013 This project and the research leading to these results has received funding
MILC Performance Benchmark and Profiling. April 2013
MILC Performance Benchmark and Profiling April 2013 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information on the supporting
Building supercomputers from commodity embedded chips
http://www.montblanc-project.eu Building supercomputers from commodity embedded chips Alex Ramirez Barcelona Supercomputing Center Technical Coordinator This project and the research leading to these results
The rcuda technology: an inexpensive way to improve the performance of GPU-based clusters Federico Silla
The rcuda technology: an inexpensive way to improve the performance of -based clusters Federico Silla Technical University of Valencia Spain The scope of this talk Delft, April 2015 2/47 More flexible
rcuda: towards energy-efficiency in GPU computing by leveraging low-power processors and InfiniBand interconnects
rcuda: towards energy-efficiency in computing by leveraging low-power processors and InfiniBand interconnects Federico Silla Technical University of Valencia Spain Joint research effort Outline Current
A176 C clone. GPGPU Fanless Small FF RediBuilt Supercomputer. Aitech
The A176 Cyclone is the smallest and most powerful Rugged-GPGPU, ideally suited for distributed systems. Its 256 CUDA cores reach 1 TFLOPS at a remarkable level of energy efficiency, providing all the
Barcelona Supercomputing Center
www.bsc.es Barcelona Supercomputing Center Centro Nacional de Supercomputación EMIT 2016. Barcelona June 2 nd, 2016 Barcelona Supercomputing Center Centro Nacional de Supercomputación BSC-CNS objectives:
Performance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA
Performance Optimizations via Connect-IB and Dynamically Connected Transport Service for Maximum Performance on LS-DYNA Pak Lui, Gilad Shainer, Brian Klaff Mellanox Technologies Abstract From concept to
HA-PACS/TCA: Tightly Coupled Accelerators for Low-Latency Communication between GPUs
HA-PACS/TCA: Tightly Coupled Accelerators for Low-Latency Communication between GPUs Yuetsu Kodama Division of High Performance Computing Systems Center for Computational Sciences University of Tsukuba,
FiPS and M2DC: Novel Architectures for Reconfigurable Hyperscale Servers
FiPS and M2DC: Novel Architectures for Reconfigurable Hyperscale Servers Rene Griessl, Meysam Peykanu, Lennart Tigges, Jens Hagemeyer, Mario Porrmann Center of Excellence Cognitive Interaction Technology
GROMACS (GPU) Performance Benchmark and Profiling. February 2016
GROMACS (GPU) Performance Benchmark and Profiling February 2016 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Mellanox, NVIDIA Compute
John Fragalla TACC 'RANGER' INFINIBAND ARCHITECTURE WITH SUN TECHNOLOGY. Presenter s Name Title and Division Sun Microsystems
TACC 'RANGER' INFINIBAND ARCHITECTURE WITH SUN TECHNOLOGY SUBTITLE WITH TWO LINES OF TEXT IF NECESSARY John Fragalla Presenter s Name Title and Division Sun Microsystems Principle Engineer High Performance
N V M e o v e r F a b r i c s -
N V M e o v e r F a b r i c s - H i g h p e r f o r m a n c e S S D s n e t w o r k e d f o r c o m p o s a b l e i n f r a s t r u c t u r e Rob Davis, VP Storage Technology, Mellanox OCP Evolution Server
GPUs and Emerging Architectures
GPUs and Emerging Architectures Mike Giles mike.giles@maths.ox.ac.uk Mathematical Institute, Oxford University e-infrastructure South Consortium Oxford e-research Centre Emerging Architectures p. 1 CPUs
n N c CIni.o ewsrg.au
@NCInews NCI and Raijin National Computational Infrastructure 2 Our Partners General purpose, highly parallel processors High FLOPs/watt and FLOPs/$ Unit of execution Kernel Separate memory subsystem GPGPU
ANSYS Fluent 14 Performance Benchmark and Profiling. October 2012
ANSYS Fluent 14 Performance Benchmark and Profiling October 2012 Note The following research was performed under the HPC Advisory Council activities Special thanks for: HP, Mellanox For more information
Carlos Reaño, Javier Prades and Federico Silla Technical University of Valencia (Spain)
Carlos Reaño, Javier Prades and Federico Silla Technical University of Valencia (Spain) 4th IEEE International Workshop of High-Performance Interconnection Networks in the Exascale and Big-Data Era (HiPINEB
VPI / InfiniBand. Performance Accelerated Mellanox InfiniBand Adapters Provide Advanced Data Center Performance, Efficiency and Scalability
VPI / InfiniBand Performance Accelerated Mellanox InfiniBand Adapters Provide Advanced Data Center Performance, Efficiency and Scalability Mellanox enables the highest data center performance with its
Embedded Computing without Compromise. Evolution of the Rugged GPGPU Computer Session: SIL7127 Dan Mor PLM -Aitech Systems GTC Israel 2017
Evolution of the Rugged GPGPU Computer Session: SIL7127 Dan Mor PLM - Systems GTC Israel 2017 Agenda Current GPGPU systems NVIDIA Jetson TX1 and TX2 evaluation Conclusions New Products 2 GPGPU Product
NAMD Performance Benchmark and Profiling. January 2015
NAMD Performance Benchmark and Profiling January 2015 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource
LAMMPSCUDA GPU Performance. April 2011
LAMMPSCUDA GPU Performance April 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory Council
Resources Current and Future Systems. Timothy H. Kaiser, Ph.D.
Resources Current and Future Systems Timothy H. Kaiser, Ph.D. tkaiser@mines.edu 1 Most likely talk to be out of date History of Top 500 Issues with building bigger machines Current and near future academic
Tightly Coupled Accelerators Architecture
Tightly Coupled Accelerators Architecture Yuetsu Kodama Division of High Performance Computing Systems Center for Computational Sciences University of Tsukuba, Japan 1 What is Tightly Coupled Accelerators
About 2CRSI. OCtoPus Solution. Technical Specifications. OCtoPus servers. OCtoPus. OCP Solution by 2CRSI.
About 2CRSI OCtoPus Solution Technical Specifications OCtoPus servers OCtoPus OCP Solution by 2CRSI 1 About 2CRSI 3 OCtoPus Solution 4 Technical Specifications OCtoPus Rack Unique server design 6 7 OCtoPus
Broadberry. Artificial Intelligence Server for Fraud. Date: Q Application: Artificial Intelligence
TM Artificial Intelligence Server for Fraud Date: Q2 2017 Application: Artificial Intelligence Tags: Artificial intelligence, GPU, GTX 1080 TI HM Revenue & Customs The UK s tax, payments and customs authority
Analyzing Performance and Power of Applications on GPUs with Dell 12G Platforms. Dr. Jeffrey Layton Enterprise Technologist HPC
Analyzing Performance and Power of Applications on GPUs with Dell 12G Platforms Dr. Jeffrey Layton Enterprise Technologist HPC Why GPUs? GPUs have very high peak compute capability! 6-9X CPU Challenges
SUN CUSTOMER READY HPC CLUSTER: REFERENCE CONFIGURATIONS WITH SUN FIRE X4100, X4200, AND X4600 SERVERS Jeff Lu, Systems Group Sun BluePrints OnLine
SUN CUSTOMER READY HPC CLUSTER: REFERENCE CONFIGURATIONS WITH SUN FIRE X4100, X4200, AND X4600 SERVERS Jeff Lu, Systems Group Sun BluePrints OnLine April 2007 Part No 820-1270-11 Revision 1.1, 4/18/07
THE LEADER IN VISUAL COMPUTING
MOBILE EMBEDDED THE LEADER IN VISUAL COMPUTING 2 TAKING OUR VISION TO REALITY HPC DESIGN and VISUALIZATION AUTO GAMING 3 BEST DEVELOPER EXPERIENCE Tools for Fast Development Debug and Performance Tuning
Next Generation Storage Networking for Next Generation Data Centers. PRESENTATION TITLE GOES HERE Dennis Martin President, Demartek
Next Generation Storage Networking for Next Generation Data Centers PRESENTATION TITLE GOES HERE Dennis Martin President, Demartek Agenda About Demartek Increased Bandwidth Needs for Storage Storage Interface
MELLANOX EDR UPDATE & GPUDIRECT MELLANOX SR. SE 정연구
MELLANOX EDR UPDATE & GPUDIRECT MELLANOX SR. SE 정연구 Leading Supplier of End-to-End Interconnect Solutions Analyze Enabling the Use of Data Store ICs Comprehensive End-to-End InfiniBand and Ethernet Portfolio
Fermi Cluster for Real-Time Hyperspectral Scene Generation
Fermi Cluster for Real-Time Hyperspectral Scene Generation Gary McMillian, Ph.D. Crossfield Technology LLC 9390 Research Blvd, Suite I200 Austin, TX 78759-7366 (512)795-0220 x151 gary.mcmillian@crossfieldtech.com
Interconnect Your Future
Interconnect Your Future Gilad Shainer 2nd Annual MVAPICH User Group (MUG) Meeting, August 2014 Complete High-Performance Scalable Interconnect Infrastructure Comprehensive End-to-End Software Accelerators
Performance Accelerated Mellanox InfiniBand Adapters Provide Advanced Data Center Performance, Efficiency and Scalability
Performance Accelerated Mellanox InfiniBand Adapters Provide Advanced Data Center Performance, Efficiency and Scalability Mellanox InfiniBand Host Channel Adapters (HCA) enable the highest data center
Interconnection Network for Tightly Coupled Accelerators Architecture
Interconnection Network for Tightly Coupled Accelerators Architecture Toshihiro Hanawa, Yuetsu Kodama, Taisuke Boku, Mitsuhisa Sato Center for Computational Sciences University of Tsukuba, Japan 1 What
About 2CRSI. OCtoPus Solution. Technical Specifications. OCtoPus. OCP Solution by 2CRSI.
About 2CRSI OCtoPus Solution Technical Specifications OCtoPus OCtoPus OCP Solution by 2CRSI 1 Remark: All specifications and photos are subject to change whitout notice. 2 About 2CRSI 5 OCtoPus Solution
Elaborazione dati real-time su architetture embedded many-core e FPGA
Elaborazione dati real-time su architetture embedded many-core e FPGA DAVIDE ROSSI A L E S S A N D R O C A P O T O N D I G I U S E P P E T A G L I A V I N I A N D R E A M A R O N G I U C I R I - I C T
fit-pc Intense 2 Overview
specifications Specifications: Overview is the second generation of the acclaimed miniature fanless PC, and further improves the best-in-class features of its predecessor with 4 th and 5 th generations
Power Systems AC922 Overview. Chris Mann IBM Distinguished Engineer Chief System Architect, Power HPC Systems December 11, 2017
Power Systems AC922 Overview Chris Mann IBM Distinguished Engineer Chief System Architect, Power HPC Systems December 11, 2017 IBM POWER HPC Platform Strategy High-performance computer and high-performance
Supercomputing with Commodity CPUs: Are Mobile SoCs Ready for HPC?
Supercomputing with Commodity CPUs: Are Mobile SoCs Ready for HPC? Nikola Rajovic, Paul M. Carpenter, Isaac Gelado, Nikola Puzovic, Alex Ramirez, Mateo Valero SC 13, November 19 th 2013, Denver, CO, USA
VPI / InfiniBand. Performance Accelerated Mellanox InfiniBand Adapters Provide Advanced Data Center Performance, Efficiency and Scalability
VPI / InfiniBand Performance Accelerated Mellanox InfiniBand Adapters Provide Advanced Data Center Performance, Efficiency and Scalability Mellanox enables the highest data center performance with its
ARM and x86 on Qseven & COM Express Mini. Zeljko Loncaric, Marketing Engineer, congatec AG
ARM and x86 on Qseven & COM Express Mini Zeljko Loncaric, Marketing Engineer, congatec AG Content COM Computer-On-Module Concept Qseven Key Points The Right ARM Integration with Freescale i.mx6 Qseven
The Cray CX1 puts massive power and flexibility right where you need it in your workgroup
The Cray CX1 puts massive power and flexibility right where you need it in your workgroup Up to 96 cores of Intel 5600 compute power 3D visualization Up to 32TB of storage GPU acceleration Small footprint
IBM Power AC922 Server
IBM Power AC922 Server The Best Server for Enterprise AI Highlights More accuracy - GPUs access system RAM for larger models Faster insights - significant deep learning speedups Rapid deployment - integrated
Resources Current and Future Systems. Timothy H. Kaiser, Ph.D.
Resources Current and Future Systems Timothy H. Kaiser, Ph.D. tkaiser@mines.edu 1 Most likely talk to be out of date History of Top 500 Issues with building bigger machines Current and near future academic
Hosted Desktops Infrastructure
Hosted Desktops Infrastructure May 2014 Aligning to PC User segmentation High Density Session / App Virtualization Virtual desktops (VDI) App / Desktop virtualization with graphics acceleration GAP Client
Comet Virtualization Code & Design Sprint
Comet Virtualization Code & Design Sprint SDSC September 23-24 Rick Wagner San Diego Supercomputer Center Meeting Goals Build personal connections between the IU and SDSC members of the Comet team working
Avid Configuration Guidelines Dell R7920 Rack workstation Dual 8 to 28 Core CPU System
Avid Configuration Guidelines Dell R7920 Rack workstation Dual 8 to 28 Core CPU System Ports & Slots 1. System Status Indicator 2. Hard drive (x8) 3. USB 3.0 connector 4. Optical-drive (optional) 5. Information
About Us. Are you ready for headache-free HPC? Call us and learn more about our custom clustering solutions.
About Us Advanced Clustering Technologies customizes solutions to meet your exact specifications. For more information regarding our services, email us at sales@advancedclustering.com Advanced Clustering
OPEN MPI WITH RDMA SUPPORT AND CUDA. Rolf vandevaart, NVIDIA
OPEN MPI WITH RDMA SUPPORT AND CUDA Rolf vandevaart, NVIDIA OVERVIEW What is CUDA-aware History of CUDA-aware support in Open MPI GPU Direct RDMA support Tuning parameters Application example Future work
Apalis A New Architecture for Embedded Computing
Apalis A New Architecture for Embedded Computing Agenda The Hardware Abstraction Pyramid The System-on-Module (SoM) Why Should You Use a SoM? Discovering Apalis Motivations Architectural Overview Standard
Manycore and GPU Channelisers. Seth Hall High Performance Computing Lab, AUT
Manycore and GPU Channelisers Seth Hall High Performance Computing Lab, AUT GPU Accelerated Computing GPU-accelerated computing is the use of a graphics processing unit (GPU) together with a CPU to accelerate
Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA TESLA GPU Cluster
Performance Analysis of Memory Transfers and GEMM Subroutines on NVIDIA TESLA GPU Cluster Veerendra Allada, Troy Benjegerdes Electrical and Computer Engineering, Ames Laboratory Iowa State University &
A step up from TS-831X
A step up from TS-831X TS-831X TS-832X Processor Quad-core AL314 Quad-core AL324 CPU architecture ARMv7 Cortex-A15 32 bit ARMv8 Cortex-A57 64 bit CPU frequency Up to 1.7 GHz 1.7 GHz Memory DDR3 DDR4 LAN
The Visual Computing Company
The Visual Computing Company Update NVIDIA GPU Ecosystem Axel Koehler, Senior Solutions Architect HPC, NVIDIA Outline Tesla K40 and GPU Boost Jetson TK-1 Development Board for Embedded HPC Pascal GPU 3D
HPC Performance in the Cloud: Status and Future Prospects
HPC Performance in the Cloud: Status and Future Prospects ISC Cloud 2012 Josh Simons, Office of the CTO, VMware 2009 VMware Inc. All rights reserved Cloud Cloud computing is a model for enabling ubiquitous,
Blue Gene/Q. Hardware Overview Michael Stephan. Mitglied der Helmholtz-Gemeinschaft
Blue Gene/Q Hardware Overview 02.02.2015 Michael Stephan Blue Gene/Q: Design goals System-on-Chip (SoC) design Processor comprises both processing cores and network Optimal performance / watt ratio Small
Mellanox Technologies Maximize Cluster Performance and Productivity. Gilad Shainer, October, 2007
Mellanox Technologies Maximize Cluster Performance and Productivity Gilad Shainer, shainer@mellanox.com October, 27 Mellanox Technologies Hardware OEMs Servers And Blades Applications End-Users Enterprise
Building supercomputers from embedded technologies
http://www.montblanc-project.eu Building supercomputers from embedded technologies Alex Ramirez Barcelona Supercomputing Center Technical Coordinator This project and the research leading to these results
Intel Xeon E v4, Optional Operating System, 8GB Memory, 2TB SAS H330 Hard Drive and a 3 Year Warranty
pe_r730_1356_a Datasheet Check its price: Click Here Overview adapts to virtually any workload with a scalable server featuring an optimal mix of memory, storage, processing and GPUs. This model is the
Vectorisation and Portable Programming using OpenCL
Vectorisation and Portable Programming using OpenCL Mitglied der Helmholtz-Gemeinschaft Jülich Supercomputing Centre (JSC) Andreas Beckmann, Ilya Zhukov, Willi Homberg, JSC Wolfram Schenck, FH Bielefeld
World s most advanced data center accelerator for PCIe-based servers
NVIDIA TESLA P100 GPU ACCELERATOR World s most advanced data center accelerator for PCIe-based servers HPC data centers need to support the ever-growing demands of scientists and researchers while staying
STAR-CCM+ Performance Benchmark and Profiling. July 2014
STAR-CCM+ Performance Benchmark and Profiling July 2014 Note The following research was performed under the HPC Advisory Council activities Participating vendors: CD-adapco, Intel, Dell, Mellanox Compute
RECENT TRENDS IN GPU ARCHITECTURES. Perspectives of GPU computing in Science, 26 th Sept 2016
RECENT TRENDS IN GPU ARCHITECTURES Perspectives of GPU computing in Science, 26 th Sept 2016 NVIDIA THE AI COMPUTING COMPANY GPU Computing Computer Graphics Artificial Intelligence 2 NVIDIA POWERS WORLD
Butterfly effect of porting scientific applications to ARM-based platforms
montblanc-project.eu @MontBlanc_EU Butterfly effect of porting scientific applications to ARM-based platforms Filippo Mantovani September 12 th, 2017 This project has received funding from the European
. Micro SD Card Socket. SMARC 2.0 Compliant
MSC SM2S-IMX6 NXP i.mx6 ARM Cortex -A9 Description The design of the MSC SM2S-IMX6 module is based on NXP s i.mx 6 processors offering quad-, dual- and single-core ARM Cortex -A9 compute performance at
INCREASE IT EFFICIENCY, REDUCE OPERATING COSTS AND DEPLOY ANYWHERE
www.iceotope.com DATA SHEET INCREASE IT EFFICIENCY, REDUCE OPERATING COSTS AND DEPLOY ANYWHERE BLADE SERVER TM PLATFORM 80% Our liquid cooling platform is proven to reduce cooling energy consumption by
HPC Hardware Overview
HPC Hardware Overview John Lockman III April 19, 2013 Texas Advanced Computing Center The University of Texas at Austin Outline Lonestar Dell blade-based system InfiniBand ( QDR) Intel Processors Longhorn
OpenFOAM Performance Testing and Profiling. October 2017
OpenFOAM Performance Testing and Profiling October 2017 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Huawei, Mellanox Compute resource - HPC
. SMARC 2.0 Compliant
MSC SM2S-IMX8 NXP i.mx8 ARM Cortex -A72/A53 Description The new MSC SM2S-IMX8 module offers a quantum leap in terms of computing and graphics performance. It integrates the currently most powerful i.mx8
Avid Configuration Guidelines Dell R7920 Rack workstation Dual 8 to 28 Core CPU System
Avid Configuration Guidelines Dell R7920 Rack workstation Dual 8 to 28 Core CPU System Ports & Slots 1. System Status Indicator 2. Hard drive (x8) 3. USB 3.0 connector 4. Optical-drive (optional) 5. Information
Gen-Z Memory-Driven Computing
Gen-Z Memory-Driven Computing Our vision for the future of computing Patrick Demichel Distinguished Technologist Explosive growth of data More Data Need answers FAST! Value of Analyzed Data 2005 0.1ZB
CST STUDIO SUITE R Supported GPU Hardware
CST STUDIO SUITE R 2017 Supported GPU Hardware 1 Supported Hardware CST STUDIO SUITE currently supports up to 8 GPU devices in a single host system, meaning each number of GPU devices between 1 and 8 is
Designed for Maximum Accelerator Performance
Designed for Maximum Accelerator Performance A dense, GPU-accelerated cluster supercomputer that delivers up to 329 double-precision GPU teraflops in one rack. This power- and spaceefficient system can
Catapult: A Reconfigurable Fabric for Petaflop Computing in the Cloud
Catapult: A Reconfigurable Fabric for Petaflop Computing in the Cloud Doug Burger Director, Hardware, Devices, & Experiences MSR NExT November 15, 2015 The Cloud is a Growing Disruptor for HPC Moore s
Specifications. Packing List. Ethernet Interface. General. MIOe Expansion Slot. VGA/HDMI Interface. MIO-2360 Startup Manual 1
MIO-236 Intel Celeron N335/Atom E394/ Atom E393 Pico-ITX SBC, DDR3L, 24-bit LVDS, VGA/HDMI, 1 GbE, Full-size Mini PCIe, 4 USB, 2 COM, SMBus, msata & MIOe Startup Manual Packing List Before you begin installing
Exploiting Task-Parallelism on GPU Clusters via OmpSs and rcuda Virtualization
Exploiting Task-Parallelism on Clusters via Adrián Castelló, Rafael Mayo, Judit Planas, Enrique S. Quintana-Ortí RePara 2015, August Helsinki, Finland Exploiting Task-Parallelism on Clusters via Power/energy/utilization
ARISTA: Improving Application Performance While Reducing Complexity
ARISTA: Improving Application Performance While Reducing Complexity October 2008 1.0 Problem Statement #1... 1 1.1 Problem Statement #2... 1 1.2 Previous Options: More Servers and I/O Adapters... 1 1.3
System Design of Kepler Based HPC Solutions. Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering.
System Design of Kepler Based HPC Solutions Saeed Iqbal, Shawn Gao and Kevin Tubbs HPC Global Solutions Engineering. Introduction The System Level View K20 GPU is a powerful parallel processor! K20 has
Avid Configuration Guidelines Lenovo P520/P520C workstation Single 6 to 18 Core CPU System P520 P520C
Avid Configuration Guidelines Lenovo P520/P520C workstation Single 6 to 18 Core CPU System P520 P520C Page 1 of 14 Dave Pimm Avid Technology April 25, 2018 1.) Lenovo P520 & P520C AVID Qualified System
Is remote GPU virtualization useful? Federico Silla Technical University of Valencia Spain
Is remote virtualization useful? Federico Silla Technical University of Valencia Spain st Outline What is remote virtualization? HPC Advisory Council Spain Conference 2015 2/57 We deal with s, obviously!
IBM Research: AcceleratorTechnologies in HPC and Cognitive Computing
MaRS Workshop, Eurosys 2017, Belgrade April 23, 2017 IBM Research: AcceleratorTechnologies in HPC and Cognitive Computing Christoph Hagleitner, hle@zurich.ibm.com IBM Research - Zurich Lab IBM Research
ASTRI/CTA data analysis on parallel and low-power platforms
ICT Workshop INAF, Cefalù 2015 Universidade de São Paulo Instituto de Astronomia, Geofisica e Ciencias Atmosferica ASTRI/CTA data analysis on parallel and low-power platforms Alberto Madonna, Michele Mastropietro
Computing on Low Power SoC Architecture
+ Computing on Low Power SoC Architecture Andrea Ferraro INFN-CNAF Lucia Morganti INFN-CNAF + Outline 2 Modern Low Power Systems on Chip Computing on System on Chip ARM CPU SoC GPU Low Power from Intel
2008 International ANSYS Conference
2008 International ANSYS Conference Maximizing Productivity With InfiniBand-Based Clusters Gilad Shainer Director of Technical Marketing Mellanox Technologies 2008 ANSYS, Inc. All rights reserved. 1 ANSYS,
Turn-key OPNsense appliances from the founders
rev. 201603 Turn-key appliances from the founders OPEN SOURCE SECURITY FOR YOUR BUSINESS 2016 Deciso B.V., All Rights Reserved. All rights reserved. No part of this publication may be reproduced, distributed,
GPU Clusters for High- Performance Computing Jeremy Enos Innovative Systems Laboratory
GPU Clusters for High- Performance Computing Jeremy Enos Innovative Systems Laboratory National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Presentation Outline NVIDIA