Remote GPU virtualization: pros and cons of a recent technology. Federico Silla Technical University of Valencia Spain
|
|
|
- Doris Perkins
- 5 years ago
- Views:
Transcription
1 Remote virtualization: pros and cons of a recent technology Federico Silla Technical University of Valencia Spain
2 The scope of this talk HPC Advisory Council Brazil Conference /43
3 st Outline What is remote virtualization? HPC Advisory Council Brazil Conference /43
4 Basics of computing Basic CUDA behavior HPC Advisory Council Brazil Conference /43
5 Remote virtualization A software technology that enables a more flexible use of s in computing facilities No HPC Advisory Council Brazil Conference /43
6 Basics of remote virtualization HPC Advisory Council Brazil Conference /43
7 Basics of remote virtualization HPC Advisory Council Brazil Conference /43
8 nd Outline Why is remote virtualization needed? HPC Advisory Council Brazil Conference /43
9 Outline Which is the problem with -enabled clusters? HPC Advisory Council Brazil Conference /43
10 Characteristics of -based clusters A -enabled cluster is a set of independent self-contained nodes that leverage the shared-nothing approach: Nothing is directly shared among nodes (MPI required for aggregating computing resources within the cluster) s can only be used within the node they are attached to Interconnection HPC Advisory Council Brazil Conference /43
11 First concern with accelerated clusters Applications can only use the s located within their node: Non-accelerated applications keep s idle in the nodes where they use all the cores A -only application spreading over these four nodes would make their s unavailable for accelerated applications Interconnection HPC Advisory Council Brazil Conference /43
12 Money leakage in current clusters? For some workloads, s may be idle for significant periods of time: Initial acquisition costs not amortized Space: s reduce density Energy: idle s keep consuming power Idle Power (Watts) 1 node 4 s node 25% 1 node: Two E5-2620V2 sockets and 32GB DDR3 RAM. One Tesla K20 4 s node: Two E5-2620V2 sockets and 128GB DDR3 RAM. Four Tesla K20 s Time (s) HPC Advisory Council Brazil Conference /43
13 Second concern with accelerated clusters Applications can only use the s located within their node: Multi- applications running on a subset of nodes cannot make use of the tremendous resources available at other cluster nodes (even if they are idle) multi- application All these s cannot be used by the multi- application in execution Interconnection HPC Advisory Council Brazil Conference /43
14 One more concern with accelerated clusters Do applications completely squeeze the s present in the cluster? Even if all s are assigned to running applications, computational resources inside s may not be fully used Application presenting low level of parallelism code being executed ( assigned working) -core stall due to lack of data etc Interconnection HPC Advisory Council Brazil Conference /43
15 Why performance in clusters is lost? In summary There are scenarios where s are available but cannot be used Accelerated applications do not make use of s 100% of the time In conclusion We are losing cycles, thus reducing cluster performance HPC Advisory Council Brazil Conference /43
16 We need something more in the cluster The current model for using s is too rigid What is missing is some flexibility for using the s in the cluster HPC Advisory Council Brazil Conference /43
17 We need something more in the cluster The current model for using s is too rigid What is missing is some flexibility for using the s in the cluster A way of seamlessly sharing s across nodes in the cluster (remote virtualization) HPC Advisory Council Brazil Conference /43
18 Remote virtualization envision Remote virtualization allows a new vision of a deployment, moving from the usual cluster configuration: Physical configuration Interconnection to the following one: Logical connections Logical configuration HPC Advisory Council Brazil Conference 2015 Interconnection 18/43
19 Remote virtualization envision Real local s Without virtualization Interconnection With virtualization Virtualized remote s virtualization allows all nodes to access all s Interconnection HPC Advisory Council Brazil Conference /43
20 Busy cores are no longer a problem Physical Interconnection configuration Logical connections Logical Interconnection configuration HPC Advisory Council Brazil Conference /43
21 Multi- applications get benefit virtualization is also useful for multi- applications Only the s in the node can be provided to the application Without virtualization Interconnection With virtualization Many s in the cluster can be provided to the application Logical connections Interconnection HPC Advisory Council Brazil Conference /43
22 Remote virtualization frameworks Several efforts have been made to implement remote virtualization during the last years: rcuda (CUDA 7.0) GVirtuS (CUDA 3.2) DS-CUDA (CUDA 4.1) vcuda (CUDA 1.1) GViM (CUDA 1.1) GridCUDA (CUDA 2.3) V- (CUDA 4.0) Publicly available NOT publicly available HPC Advisory Council Brazil Conference /43
23 Remote virtualization frameworks InfiniBand FDR + K20!! H2D pageable D2H pageable H2D pinned D2H pinned HPC Advisory Council Brazil Conference /43
24 rd Outline Cons of remote virtualization? HPC Advisory Council Brazil Conference /43
25 Problem with remote virtualization The main virtualization drawback is the reduced bandwidth to the remote No HPC Advisory Council Brazil Conference /43
26 rcuda transfers are optimized H2D pageable D2H pageable Almost 100% of available BW H2D pinned Almost 100% of available BW D2H pinned HPC Advisory Council Brazil Conference /43
27 Rodinia performance with rcuda InfiniBand EDR + K40!! HPC Advisory Council Brazil Conference /43
28 Application performance with rcuda InfiniBand EDR + K40!! HPC Advisory Council Brazil Conference /43
29 th Outline Pros of remote virtualization? HPC Advisory Council Brazil Conference /43
30 1: more s for a single application As many s as there are in the cluster may be provided to a single application No HPC Advisory Council Brazil Conference /43
31 1: more s for a single application HPC Advisory Council Brazil Conference /43
32 1: more s for a single application MonteCarlo Multi- (from NVIDIA samples) Higher is better Lower is better HPC Advisory Council Brazil Conference /43
33 2: increased cluster performance s can be shared among jobs running in remote clients App 1 App 2 App 3 App 4 App 5 App 6 App 7 App 8 App 9 HPC Advisory Council Brazil Conference /43
34 2: increased cluster performance Test bench for studying rcuda performance at cluster level: SLURM used as job scheduler InfiniBand ConnectX-3 based cluster Dual socket E5-2620v2 Intel Xeon based nodes: 1 node without 8 nodes with NVIDIA K20 Four applications used LAMMPS -Blast MCUDA-MEME Gromacs (no ) Three workload sizes: Small Medium Large 1 node hosting the main SLURM controller 8 nodes with one K20 each SLURM v15.08 includes support for rcuda!! HPC Advisory Council Brazil Conference /43
35 2: increased cluster performance HPC Advisory Council Brazil Conference /43
36 3: more performance with less cost Let s reduce the amount of s in the cluster 43% Less 41% Less 42% Less HPC Advisory Council Brazil Conference /43
37 4: reduced energy consumption HPC Advisory Council Brazil Conference /43
38 Ongoing work Non- 16-node cluster being used Analysis of different assignment policies Based on -ory occupancy Based on utilization More applications used for tests: -Blast (21 s execution time) LAMMPS (15 s execution time) MCUDA-MEME (165 s execution time) GROMACS (167 s execution time) NAMD (11 m execution time) BarraCUDA (10 m execution time) -LIBSVM (5 m execution time) MUMmer (5 m execution time) Short execution time Long execution time HPC Advisory Council Brazil Conference /43
39 5: easier cluster upgrade A cluster without s may be easily upgraded to use s with rcuda No HPC Advisory Council Brazil Conference /43
40 5: easier cluster upgrade A cluster without s may be easily upgraded to use s with rcuda No HPC Advisory Council Brazil Conference /43
41 6: task migration Box A has 4 s but only one is busy Box B has 8 s but only two are busy 1. Move jobs from Box B to Box A and switch off Box B 2. Migration should be transparent to applications (decided by the global scheduler) Box B Box A TRUE GREEN COMPUTING HPC Advisory Council Brazil Conference /43
42 rcuda is the enabling technology for High Throughput Computing Sharing remote s makes applications to execute slower BUT more throughput (jobs/time) is achieved Datacenter administrators can choose between HPC and HTC Green Computing migration and application migration allow to devote just the required computing resources to the current workload More flexible system upgrades and updates become independent from each other. Attaching boxes to non -enabled clusters is possible HPC Advisory Council Brazil Conference /43
43 Get a free copy of rcuda at More than 600 requests world The rcuda Team Carlos Reaño Federico Silla Fernando Campos José Duato Javier Prades rcuda is owned by Technical University of Valencia HPC Advisory Council Brazil Conference /43
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!
Deploying remote GPU virtualization with rcuda. Federico Silla Technical University of Valencia Spain
Deploying remote virtualization with rcuda Federico Silla Technical University of Valencia Spain st Outline What is remote virtualization? HPC ADMINTECH 2016 2/53 It deals with s, obviously! HPC ADMINTECH
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: hybrid CPU-GPU clusters Federico Silla Technical University of Valencia Spain
rcuda: hybrid - clusters Federico Silla Technical University of Valencia Spain Outline 1. Hybrid - clusters 2. Concerns with hybrid clusters 3. One possible solution: virtualize s! 4. rcuda what s that?
Speeding up the execution of numerical computations and simulations with rcuda José Duato
Speeding up the execution of numerical computations and simulations with rcuda José Duato Universidad Politécnica de Valencia Spain Outline 1. Introduction to GPU computing 2. What is remote GPU virtualization?
Opportunities of the rcuda remote GPU virtualization middleware. Federico Silla Universitat Politècnica de València Spain
Opportunities of the rcuda remote virtualization middleware Federico Silla Universitat Politècnica de València Spain st Outline What is rcuda? HPC Advisory Council China Conference 2017 2/45 s are the
Improving overall performance and energy consumption of your cluster with remote GPU virtualization
Improving overall performance and energy consumption of your cluster with remote GPU virtualization Federico Silla & Carlos Reaño Technical University of Valencia Spain Tutorial Agenda 9.00-10.00 SESSION
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
rcuda: desde máquinas virtuales a clústers mixtos CPU-GPU
rcuda: desde máquinas virtuales a clústers mixtos CPU-GPU Federico Silla Universitat Politècnica de València HPC ADMINTECH 2018 rcuda: from virtual machines to hybrid CPU-GPU clusters Federico Silla Universitat
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
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
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,
Framework of rcuda: An Overview
Framework of rcuda: An Overview Mohamed Hussain 1, M.B.Potdar 2, Third Viraj Choksi 3 11 Research scholar, VLSI & Embedded Systems, Gujarat Technological University, Ahmedabad, India 2 Project Director,
Carlos Reaño Universitat Politècnica de València (Spain) HPC Advisory Council Switzerland Conference April 3, Lugano (Switzerland)
Carlos Reaño Universitat Politècnica de València (Spain) Switzerland Conference April 3, 2014 - Lugano (Switzerland) What is rcuda? Installing and using rcuda rcuda over HPC networks InfiniBand How taking
rcuda: an approach to provide remote access to GPU computational power
rcuda: an approach to provide remote access to computational power Rafael Mayo Gual Universitat Jaume I Spain (1 of 60) HPC Advisory Council Workshop Outline computing Cost of a node rcuda goals rcuda
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
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
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
On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications
On the Use of Remote GPUs and Low-Power Processors for the Acceleration of Scientific Applications A. Castelló, J. Duato, R. Mayo, A. J. Peña, E. S. Quintana-Ortí, V. Roca, F. Silla Universitat Politècnica
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
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING Table of Contents: The Accelerated Data Center Optimizing Data Center Productivity Same Throughput with Fewer Server Nodes
Design of a Virtualization Framework to Enable GPU Sharing in Cluster Environments
Design of a Virtualization Framework to Enable GPU Sharing in Cluster Environments Michela Becchi University of Missouri nps.missouri.edu GPUs in Clusters & Clouds Many-core GPUs are used in supercomputers
AcuSolve Performance Benchmark and Profiling. October 2011
AcuSolve Performance Benchmark and Profiling October 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox, Altair Compute
An approach to provide remote access to GPU computational power
An approach to provide remote access to computational power University Jaume I, Spain Joint research effort 1/84 Outline computing computing scenarios Introduction to rcuda rcuda structure rcuda functionality
computational power computational
rcuda: rcuda: an an approach approach to to provide provide remote remote access access to to computational computational power power Rafael Mayo Gual Universitat Jaume I Spain (1 of 59) HPC Advisory Council
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING Accelerated computing is revolutionizing the economics of the data center. HPC and hyperscale customers deploy accelerated
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
GROMACS Performance Benchmark and Profiling. August 2011
GROMACS Performance Benchmark and Profiling August 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute resource
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
AMBER 11 Performance Benchmark and Profiling. July 2011
AMBER 11 Performance Benchmark and Profiling July 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource -
Why? High performance clusters: Fast interconnects Hundreds of nodes, with multiple cores per node Large storage systems Hardware accelerators
Remote CUDA (rcuda) Why? High performance clusters: Fast interconnects Hundreds of nodes, with multiple cores per node Large storage systems Hardware accelerators Better performance-watt, performance-cost
CP2K Performance Benchmark and Profiling. April 2011
CP2K Performance Benchmark and Profiling April 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC
NAMD GPU Performance Benchmark. March 2011
NAMD GPU Performance Benchmark March 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Dell, Intel, Mellanox Compute resource - HPC Advisory
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING
TECHNICAL OVERVIEW ACCELERATED COMPUTING AND THE DEMOCRATIZATION OF SUPERCOMPUTING Accelerated computing is revolutionizing the economics of the data center. HPC enterprise and hyperscale customers deploy
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
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
The Road to ExaScale. Advances in High-Performance Interconnect Infrastructure. September 2011
The Road to ExaScale Advances in High-Performance Interconnect Infrastructure September 2011 diego@mellanox.com ExaScale Computing Ambitious Challenges Foster Progress Demand Research Institutes, Universities
CUDA Accelerated Linpack on Clusters. E. Phillips, NVIDIA Corporation
CUDA Accelerated Linpack on Clusters E. Phillips, NVIDIA Corporation Outline Linpack benchmark CUDA Acceleration Strategy Fermi DGEMM Optimization / Performance Linpack Results Conclusions LINPACK Benchmark
Slurm Configuration Impact on Benchmarking
Slurm Configuration Impact on Benchmarking José A. Moríñigo, Manuel Rodríguez-Pascual, Rafael Mayo-García CIEMAT - Dept. Technology Avda. Complutense 40, Madrid 28040, SPAIN Slurm User Group Meeting 16
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
GROMACS Performance Benchmark and Profiling. September 2012
GROMACS Performance Benchmark and Profiling September 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource
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
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
Game-changing Extreme GPU computing with The Dell PowerEdge C4130
Game-changing Extreme GPU computing with The Dell PowerEdge C4130 A Dell Technical White Paper This white paper describes the system architecture and performance characterization of the PowerEdge C4130.
Oncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries
Oncilla - a Managed GAS Runtime for Accelerating Data Warehousing Queries Jeffrey Young, Alex Merritt, Se Hoon Shon Advisor: Sudhakar Yalamanchili 4/16/13 Sponsors: Intel, NVIDIA, NSF 2 The Problem Big
LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance
11 th International LS-DYNA Users Conference Computing Technology LS-DYNA Best-Practices: Networking, MPI and Parallel File System Effect on LS-DYNA Performance Gilad Shainer 1, Tong Liu 2, Jeff Layton
Building NVLink for Developers
Building NVLink for Developers Unleashing programmatic, architectural and performance capabilities for accelerated computing Why NVLink TM? Simpler, Better and Faster Simplified Programming No specialized
LS-DYNA Performance Benchmark and Profiling. October 2017
LS-DYNA Performance Benchmark and Profiling October 2017 2 Note The following research was performed under the HPC Advisory Council activities Participating vendors: LSTC, Huawei, Mellanox Compute resource
GPU ACCELERATED COMPUTING. 1 st AlsaCalcul GPU Challenge, 14-Jun-2016, Strasbourg Frédéric Parienté, Tesla Accelerated Computing, NVIDIA Corporation
GPU ACCELERATED COMPUTING 1 st AlsaCalcul GPU Challenge, 14-Jun-2016, Strasbourg Frédéric Parienté, Tesla Accelerated Computing, NVIDIA Corporation GAMING PRO ENTERPRISE VISUALIZATION DATA CENTER AUTO
Interconnect Your Future
#OpenPOWERSummit Interconnect Your Future Scot Schultz, Director HPC / Technical Computing Mellanox Technologies OpenPOWER Summit, San Jose CA March 2015 One-Generation Lead over the Competition Mellanox
Operating System Support for Shared-ISA Asymmetric Multi-core Architectures
Operating System Support for Shared-ISA Asymmetric Multi-core Architectures Tong Li, Paul Brett, Barbara Hohlt, Rob Knauerhase, Sean McElderry, Scott Hahn Intel Corporation Contact: tong.n.li@intel.com
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,
MM5 Modeling System Performance Research and Profiling. March 2009
MM5 Modeling System Performance Research and Profiling March 2009 Note The following research was performed under the HPC Advisory Council activities AMD, Dell, Mellanox HPC Advisory Council Cluster Center
ACCELERATED COMPUTING: THE PATH FORWARD. Jen-Hsun Huang, Co-Founder and CEO, NVIDIA SC15 Nov. 16, 2015
ACCELERATED COMPUTING: THE PATH FORWARD Jen-Hsun Huang, Co-Founder and CEO, NVIDIA SC15 Nov. 16, 2015 COMMODITY DISRUPTS CUSTOM SOURCE: Top500 ACCELERATED COMPUTING: THE PATH FORWARD It s time to start
HPC Middle East. KFUPM HPC Workshop April Mohamed Mekias HPC Solutions Consultant. Agenda
KFUPM HPC Workshop April 29-30 2015 Mohamed Mekias HPC Solutions Consultant Agenda 1 Agenda-Day 1 HPC Overview What is a cluster? Shared v.s. Distributed Parallel v.s. Massively Parallel Interconnects
NAMD Performance Benchmark and Profiling. February 2012
NAMD Performance Benchmark and Profiling February 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource -
The Stampede is Coming: A New Petascale Resource for the Open Science Community
The Stampede is Coming: A New Petascale Resource for the Open Science Community Jay Boisseau Texas Advanced Computing Center boisseau@tacc.utexas.edu Stampede: Solicitation US National Science Foundation
CESM (Community Earth System Model) Performance Benchmark and Profiling. August 2011
CESM (Community Earth System Model) Performance Benchmark and Profiling August 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell,
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
PART-I (B) (TECHNICAL SPECIFICATIONS & COMPLIANCE SHEET) Supply and installation of High Performance Computing System
INSTITUTE FOR PLASMA RESEARCH (An Autonomous Institute of Department of Atomic Energy, Government of India) Near Indira Bridge; Bhat; Gandhinagar-382428; India PART-I (B) (TECHNICAL SPECIFICATIONS & COMPLIANCE
HPC Architectures. Types of resource currently in use
HPC Architectures Types of resource currently in use Reusing this material This work is licensed under a Creative Commons Attribution- NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/deed.en_us
Habanero Operating Committee. January
Habanero Operating Committee January 25 2017 Habanero Overview 1. Execute Nodes 2. Head Nodes 3. Storage 4. Network Execute Nodes Type Quantity Standard 176 High Memory 32 GPU* 14 Total 222 Execute Nodes
High Performance Computing with Accelerators
High Performance Computing with Accelerators Volodymyr Kindratenko Innovative Systems Laboratory @ NCSA Institute for Advanced Computing Applications and Technologies (IACAT) National Center for Supercomputing
The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations
The Effect of In-Network Computing-Capable Interconnects on the Scalability of CAE Simulations Ophir Maor HPC Advisory Council ophir@hpcadvisorycouncil.com The HPC-AI Advisory Council World-wide HPC non-profit
E4-ARKA: ARM64+GPU+IB is Now Here Piero Altoè. ARM64 and GPGPU
E4-ARKA: ARM64+GPU+IB is Now Here Piero Altoè ARM64 and GPGPU 1 E4 Computer Engineering Company E4 Computer Engineering S.p.A. specializes in the manufacturing of high performance IT systems of medium
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
DESIGN, IMPLEMENTATION, AND OPERATION OF IPV6-ONLY IAAS SYSTEM WITH IPV4-IPV6 TRANSLATOR FOR TRANSITION TOWARD THE FUTURE INTERNET DATACENTER
DESIGN, IMPLEMENTATION, AND OPERATION OF IPV6-ONLY IAAS SYSTEM WITH IPV4-IPV6 TRANSLATOR FOR TRANSITION TOWARD THE FUTURE INTERNET DATACENTER Keiichi SHIMA 1 (Presenter) Wataru ISHIDA 2 Yuji SEKIYA 2 1
Scheduling Strategies for HPC as a Service (HPCaaS) for Bio-Science Applications
Scheduling Strategies for HPC as a Service (HPCaaS) for Bio-Science Applications Sep 2009 Gilad Shainer, Tong Liu (Mellanox); Jeffrey Layton (Dell); Joshua Mora (AMD) High Performance Interconnects for
NVIDIA GRID. Ralph Stocker, GRID Sales Specialist, Central Europe
NVIDIA GRID Ralph Stocker, GRID Sales Specialist, Central Europe rstocker@nvidia.com GAMING AUTO ENTERPRISE HPC & CLOUD TECHNOLOGY THE WORLD LEADER IN VISUAL COMPUTING PERFORMANCE DELIVERED FROM THE CLOUD
ANSYS Improvements to Engineering Productivity with HPC and GPU-Accelerated Simulation
ANSYS Improvements to Engineering Productivity with HPC and GPU-Accelerated Simulation Ray Browell nvidia Technology Theater SC12 1 2012 ANSYS, Inc. nvidia Technology Theater SC12 HPC Revolution Recent
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
Shadowfax: Scaling in Heterogeneous Cluster Systems via GPGPU Assemblies
Shadowfax: Scaling in Heterogeneous Cluster Systems via GPGPU Assemblies Alexander Merritt, Vishakha Gupta, Abhishek Verma, Ada Gavrilovska, Karsten Schwan {merritt.alex,abhishek.verma}@gatech.edu {vishakha,ada,schwan}@cc.gtaech.edu
The Stampede is Coming Welcome to Stampede Introductory Training. Dan Stanzione Texas Advanced Computing Center
The Stampede is Coming Welcome to Stampede Introductory Training Dan Stanzione Texas Advanced Computing Center dan@tacc.utexas.edu Thanks for Coming! Stampede is an exciting new system of incredible power.
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
unleashed the future Intel Xeon Scalable Processors for High Performance Computing Alexey Belogortsev Field Application Engineer
the future unleashed Alexey Belogortsev Field Application Engineer Intel Xeon Scalable Processors for High Performance Computing Growing Challenges in System Architecture The Walls System Bottlenecks Divergent
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
Document downloaded from:
Document downloaded from: http://hdl.handle.net/10251/70225 This paper must be cited as: Reaño González, C.; Silla Jiménez, F. (2015). On the Deployment and Characterization of CUDA Teaching Laboratories.
Altair RADIOSS Performance Benchmark and Profiling. May 2013
Altair RADIOSS Performance Benchmark and Profiling May 2013 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Altair, AMD, Dell, Mellanox Compute
Altair OptiStruct 13.0 Performance Benchmark and Profiling. May 2015
Altair OptiStruct 13.0 Performance Benchmark and Profiling May 2015 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox Compute
In-Network Computing. Paving the Road to Exascale. June 2017
In-Network Computing Paving the Road to Exascale June 2017 Exponential Data Growth The Need for Intelligent and Faster Interconnect -Centric (Onload) Data-Centric (Offload) Must Wait for the Data Creates
Exploiting InfiniBand and GPUDirect Technology for High Performance Collectives on GPU Clusters
Exploiting InfiniBand and Direct Technology for High Performance Collectives on Clusters Ching-Hsiang Chu chu.368@osu.edu Department of Computer Science and Engineering The Ohio State University OSU Booth
NLVMUG 16 maart Display protocols in Horizon
NLVMUG 16 maart 2017 Display protocols in Horizon NLVMUG 16 maart 2017 Display protocols in Horizon Topics Introduction Display protocols - Basics PCoIP vs Blast Extreme Optimizing Monitoring Future Recap
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
Himeno Performance Benchmark and Profiling. December 2010
Himeno Performance Benchmark and Profiling December 2010 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource
Mapping MPI+X Applications to Multi-GPU Architectures
Mapping MPI+X Applications to Multi-GPU Architectures A Performance-Portable Approach Edgar A. León Computer Scientist San Jose, CA March 28, 2018 GPU Technology Conference This work was performed under
High Performance Computing
High Performance Computing Dror Goldenberg, HPCAC Switzerland Conference March 2015 End-to-End Interconnect Solutions for All Platforms Highest Performance and Scalability for X86, Power, GPU, ARM and
The Future of High Performance Interconnects
The Future of High Performance Interconnects Ashrut Ambastha HPC Advisory Council Perth, Australia :: August 2017 When Algorithms Go Rogue 2017 Mellanox Technologies 2 When Algorithms Go Rogue 2017 Mellanox
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
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator Servers
A PCIe Congestion-Aware Performance Model for Densely Populated Accelerator Servers Maxime Martinasso, Grzegorz Kwasniewski, Sadaf R. Alam, Thomas C. Schulthess, Torsten Hoefler Swiss National Supercomputing
AcuSolve Performance Benchmark and Profiling. October 2011
AcuSolve Performance Benchmark and Profiling October 2011 Note The following research was performed under the HPC Advisory Council activities Participating vendors: Intel, Dell, Mellanox, Altair Compute
Technical guide. Windows HPC server 2016 for LS-DYNA How to setup. Reference system setup - v1.0
Technical guide Windows HPC server 2016 for LS-DYNA How to setup Reference system setup - v1.0 2018-02-17 2018 DYNAmore Nordic AB LS-DYNA / LS-PrePost 1 Introduction - Running LS-DYNA on Windows HPC cluster
Two-Phase flows on massively parallel multi-gpu clusters
Two-Phase flows on massively parallel multi-gpu clusters Peter Zaspel Michael Griebel Institute for Numerical Simulation Rheinische Friedrich-Wilhelms-Universität Bonn Workshop Programming of Heterogeneous
Making Supercomputing More Available and Accessible Windows HPC Server 2008 R2 Beta 2 Microsoft High Performance Computing April, 2010
Making Supercomputing More Available and Accessible Windows HPC Server 2008 R2 Beta 2 Microsoft High Performance Computing April, 2010 Windows HPC Server 2008 R2 Windows HPC Server 2008 R2 makes supercomputing
SR-IOV Support for Virtualization on InfiniBand Clusters: Early Experience
SR-IOV Support for Virtualization on InfiniBand Clusters: Early Experience Jithin Jose, Mingzhe Li, Xiaoyi Lu, Krishna Kandalla, Mark Arnold and Dhabaleswar K. (DK) Panda Network-Based Computing Laboratory
HOKUSAI System. Figure 0-1 System diagram
HOKUSAI System October 11, 2017 Information Systems Division, RIKEN 1.1 System Overview The HOKUSAI system consists of the following key components: - Massively Parallel Computer(GWMPC,BWMPC) - Application
LAMMPS Performance Benchmark and Profiling. July 2012
LAMMPS Performance Benchmark and Profiling July 2012 Note The following research was performed under the HPC Advisory Council activities Participating vendors: AMD, Dell, Mellanox Compute resource - HPC
TESLA P100 PERFORMANCE GUIDE. HPC and Deep Learning Applications
TESLA P PERFORMANCE GUIDE HPC and Deep Learning Applications MAY 217 TESLA P PERFORMANCE GUIDE Modern high performance computing (HPC) data centers are key to solving some of the world s most important
NVIDIA GPUDirect Technology. NVIDIA Corporation 2011
NVIDIA GPUDirect Technology NVIDIA GPUDirect : Eliminating CPU Overhead Accelerated Communication with Network and Storage Devices Peer-to-Peer Communication Between GPUs Direct access to CUDA memory for
MPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA
MPI Optimizations via MXM and FCA for Maximum Performance on LS-DYNA Gilad Shainer 1, Tong Liu 1, Pak Lui 1, Todd Wilde 1 1 Mellanox Technologies Abstract From concept to engineering, and from design to