Introduction to HPC - Virginia Tech · AdvancedResearchCompu)ng* Should I Pursue HPC for my Problem? • Arelocalresourcesinsuﬃcienttomeetyour* needs? – Verylargejobs*

Introduc)on to High Performance Compu)ng Advanced Research Computing

Advanced Research Compu)ng

Outline

•  What cons)tutes high performance compu)ng (HPC)?

•  When to consider HPC resources •  What kind of problems are typically solved? •  What are the components of HPC? •  What resources are available? •  Overview of HPC Resources at Virginia Tech

2


Should I Pursue HPC for my Problem?

•  Are local resources insufficient to meet your needs? – Very large jobs – Very many jobs – Large data

•  Do you have na)onal collaborators? – Share projects between different en))es – Convenient mechanisms for data sharing

3


Who Uses HPC?

Physics (91) 19%

Molecular Biosciences (271)

17%

Astronomical Sciences (115)

13%

Atmospheric Sciences (72)

11%

Materials Research (131)

9%

Chemical, Thermal Sys (89)

8%

Chemistry (161) 7%

ScienEfic CompuEng (60)

2%

Earth Sci (29) 2%

Training (51) 2%

•  >2 billion cpu-‐hours allocated

•  1400 alloca)ons •  350 ins)tu)ons •  32 research domains


Learning Curve

•  Linux: Command-‐line interface •  Scheduler: Shares resources among mul)ple users

•  Parallel Compu)ng: – Need to parallelize code to take advantage of supercomputer’s resources

– Third party programs or libraries make this easier


Popular SoYware Packages

•  Molecular Dynamics: Gromacs, LAMMPS •  CFD: OpenFOAM, Ansys •  Finite Elements: Deal II, Abaqus •  Chemistry: VASP, Gaussian •  Climate: CESM •  Bioinforma)cs: Mothur, QIIME, MPIBLAST •  Numerical Compu)ng/Sta)s)cs: R, Matlab •  Visualiza)on: ParaView, Ensight

Advanced Research Compu)ng 8

WHAT IS PARALLEL COMPUTING?


Parallel Compu)ng 101 •  Parallel compu)ng: use of mul)ple processors or

computers working together on a common task. –  Each processor works on its sec)on of the problem –  Processors can exchange informa)on

Grid of Problem to be solved

CPU #1 works on this area of the problem




y

x

exchange exchange

exchange

exchange exchange


Why Do Parallel Compu)ng? •  Limits of single CPU compu)ng

–  performance –  available memory –  I/O rates

•  Parallel compu)ng allows one to: –  solve problems that don’t fit on a single CPU –  solve problems that can’t be solved in a reasonable )me

•  We can solve… –  larger problems –  faster –  more cases


A Change in Moore’s Law


Parallelism is the New Moore’s Law

•  Power and energy efficiency impose a key constraint on design of micro-‐architectures

•  Clock speeds have plateaued

•  Hardware parallelism is increasing rapidly to make up the difference


WHAT DOES A MODERN SUPERCOMPUTER LOOK LIKE?


Essential Components of HPC – Supercompu)ng resources – Storage – Visualiza)on – Data management – Network infrastructure – Support

15


Blade : Rack : System •  1 node : 2 x 8 cores = 16 cores •  1 chassis : 10 nodes = 160 cores •  1 rack (frame) : 4 chassis = 640 cores •  system : 10 racks = 6,400 cores

x 4 x 10


Shared and distributed memory

•  All processors have access to a

pool of shared memory

•  Access )mes vary from CPU to CPU in NUMA systems

•  Example: SGI UV, CPUs on same node

•  Memory is local to each processor

•  Data exchange by message passing over a network

•  Example: Clusters with single-‐socket blades

P

Memory

P P P P P P P P P

M M MM M

Network


HPC Trends

Architecture Code

Single core Serial

Mul)core OpenMP, Pthreads

GPU CUDA, OpenACC

Cluster MPI

P

MGPU

Memory Memory


How are accelerators different? Intel Xeon E5-‐2670

(CPU) Intel Xeon Phi 5110P

(MIC) Nvidia Tesla K20X

(GPU)

Cores 8 60 14 SMX

Logical Cores 16 240 2,688 CUDA cores

Frequency 2.60 GHz 1.05 GHz 0.74 MHz

GFLOPs (double) 333 1,010 1,317

Memory 64 GB 8GB 6GB

Memory B/W 51.2GB/s 320GB/s 250GB/s


Mul)-‐core systems

•  Current processors place mul)ple processor cores on a die •  Communica)on details are increasingly complex

–  Cache access –  Main memory access –  Quick Path / Hyper Transport socket connec)ons –  Node to node connec)on via network

Memory

Network

Memory Memory Memory Memory


Accelerator-‐based Systems

•  Calcula)ons made in both CPUs and Graphical Processing Unit

•  No longer limited to single precision calcula)ons

•  Load balancing cri)cal for performance

•  Requires specific libraries and compilers (CUDA, OpenCL)

•  Co-‐processor from Intel: MIC (Many Integrated Core)

Network

GPU

Memory

GPU

Memory

GPU

Memory

GPU

Memory

Batch Submission Process

Internet

qsub job

Queue: Job script waits for resources. Master: Compute node that executes the job

script, launches all MPI processes.

Compute Nodes

mpirun –np # ./a.out

ibrun ./a.out

Queue

Master Node C1 C3 C2 ssh

Login Node


ARC OVERVIEW


Advanced Research Compu)ng (ARC)

•  Unit within the Office of the Vice President of Informa)on Technology

•  Provide centralized resources for: – Research compu)ng – Visualiza)on

•  Staff to assist users •  Website: hmp://www.arc.vt.edu/


Goals

•  Advance the use of compu)ng and visualiza)on in VT research

•  Centralize resource acquisi)on, maintenance, and support for research community

•  Provide support to facilitate usage of resources and minimize barriers to entry

•  Enable and par)cipate in research collabora)ons between departments


Personnel •  Associate VP for Research Compu)ng: Terry Herdman

•  Director, HPC: Vijay Agarwala •  Director, Visualiza)on: Nicholas Polys •  Computa)onal Scien)sts

–  Jus)n Krome)s –  James McClure – Brian Marshall – Srinivas Yarlanki – Srijith Rajamohan


Personnel (Con)nued) •  System Administrators

– Tim Rhodes – Chris Snapp – Brandon Sawyers

•  Vis & Virtual Reality Specialist: Wole Oyekoya •  Business Manager: Alana Romanella •  User Support GRAs: Umar Kalim and Di Zhang


Computa)onal Resources Name BlueRidge HokieSpeed HokieOne Ithaca

Key Features, Uses Large-‐scale CPU or MIC GPU Shared Memory Beginners, MATLAB

Available March 2013 Sept 2012 Apr 2012 Fall 2009

Theore)cal Peak (TFlops/s) 398.7 238.2 5.4 6.1

Nodes 408 201 N/A 79

Cores 6,528 2,412 492 632

Cores/Node 16 12 N/A* 8

Accelerators/Coprocessors

260 Intel Xeon Phi 8 Nvidia K40 GPU 408 Nvidia Tesla GPU N/A N/A

Memory Size 27.3 TB 5.0 TB 2.62 TB 2 TB

Memory/Core 4 GB* 2 GB 5.3 GB 3 GB*

Memory/Node 64 GB* 24 GB N/A* 24 GB*


Visualiza)on Resources

•  VisCube: 3D immersion environment with three 10ʹ′ by 10ʹ′ walls and a floor of 1920×1920 stereo projec)on screens

•  DeepSix: Six )led monitors with combined resolu)on of 7680×3200

•  ROVR Stereo Wall •  AISB Stereo Wall


Gewng Started on ARC Systems

1.  Review ARC’s system specifica)ons and choose the right system(s) for you a.  Specialty soYware

2.  Apply for an account online the Advanced Research Compu)ng website

3.  When your account is ready, you will receive confirma)on from ARC’s system administrators


Resources

•  ARC Website: hmp://www.arc.vt.edu •  ARC Compute Resources & Documenta)on: hmp://www.arc.vt.edu/resources/hpc/

•  New Users Guide: hmp://www.arc.vt.edu/userinfo/newusers.php

•  Frequently Asked Ques)ons: hmp://www.arc.vt.edu/userinfo/faq.php

•  Linux Introduc)on: hmp://www.arc.vt.edu/resources/soYware/unix/


Thank you.

Ques)ons?

Documents

Introduction to HPC - Virginia Tech · Advanced*Research*Compu)ng* Should I Pursue HPC for my Problem? • Are*local*resources*insuﬃcientto*meetyour* needs? – Very*large*jobs*

Introduction to HPC - Virginia Tech · AdvancedResearchCompu)ng* Should I Pursue HPC for my Problem? • Arelocalresourcesinsuﬃcienttomeetyour* needs? – Verylargejobs*