1. Taking the Complexity out of Cluster Computing Vendor Update HPC User Forum Arend Dittmer Director Product Management HPC April,17 2009

2. Copyright © 2009 Penguin Computing, Inc. All rights reserved Penguin Vision and Focus  Founded 1998 – One of HPC industry’s longest track records of success  Donald Becker, CTO – Inventor of Beowulf architecture and primary contributor to Linux kernel  Over 2500 Customers in Enterprise, Academia and Government  Focus on integrated ‘turnkey’ HPC clusters

3. Copyright © 2009 Penguin Computing, Inc. All rights reserved  Rack Integration  Software Integration Scyld Clusterware Schedulers Development tools Applications  Solution Testing System level burn- in Full cluster testing  24x7 Support  Software >Cluster Management >Applications and Workload Managers >Compilers and Tools  Hardware >Servers >GPU Accelerators >Storage >Interconnects >Racks and PDU’s Penguin Solutions Delivered “Ready-to-Run”

4. Trends in Cluster Computing Cluster Management Software

5. Copyright © 2009 Penguin Computing, Inc. All rights reserved  Linux clusters deliver unmatched price/performance  Linux clusters dominate the HPC Market (Market share >75%) however… Compute power delivered by many systems introduces complexity >Configuration consistency >Distributed applications >Workload Management  Scyld ClusterWare designed to make cluster management easy 5 The HPC Cluster Management Challenge

6. Copyright © 2009 Penguin Computing, Inc. All rights reserved 6 Scyld ClusterWare Design  Master node is the single point of control  Compute nodes are attached 'stateless' memory and processor resources  Scyld maintains consistency across the cluster Designed for Ease-of-Use and Manageability ‘Manage a Cluster like a Single System’

7. Copyright © 2009 Penguin Computing, Inc. All rights reserved Web Based Monitoring Framwork  One web based interface to all HPC cluster components  Integrates existing tools e.g. IPMI, Ganglia, TORQUE  Customizable, extensible Framework >Based on XML, Java script and ExtJS

8. Trends in Cluster Computing Hardware

9. Copyright © 2009 Penguin Computing, Inc. All rights reserved 9 Heterogeneous Computing: GPUs + CPUs  Massive processing power introduces I/O challenge >Getting data to and from the processing units can take as long as the processing itself >Requires careful software design and deep understanding of algorithms and architecture of  Processors (Cache effects, memory bandwidth)  GPU accelerators  Interconnects (Ethernet, IB, 10 Gigabit Ethernet),  Storage (local disks, NFS, parallel file systems)

10. Copyright © 2009 Penguin Computing, Inc. All rights reserved 10 Application Case Study: ANSYS / Acceleware  ANSYS Direct Sparse Solver (DSS) - Single System Mode  Matrix Decomposition offloaded to NVIDIA Tesla C1060 GPU Accelerator  ANSYS standard benchmark BM-7 – 500K-1750K DoF  Overall speedup up to 3.7X for Single Precision runs, 2.7X for Double Precision

11. Copyright © 2009 Penguin Computing, Inc. All rights reserved Sample of Penguin’s Advanced Compute Offering  NVIDIA Tesla S1070 GPU Accelerator >Four processors, 240 cores each >Native double precision floating point support >Supports Nvidia’s CUDA API  Niveus HTX Personal Supercomputer >Engineered to support Tesla coprocessors >720 GPU cores  Relion Intel 1702 > 1U Chassis housing two independent x86 nodes > Two Xeon 5500 Series 'Nehalem' processors per node >Up to 96GB of RAM on each node

12. Thank You April,17 2009

13. Copyright © 2009 Penguin Computing, Inc. All rights reserved 13 Application Case Study: ANSYS / Acceleware  ANSYS >Direct Sparse Solver (DSS) - SMP/Single System Mode  Acceleware Plug-In for ANSYS >Matrix Decomposition offloaded to NVIDIA Tesla C1060 GPU Accelerator  Benchmark >ANSYS standard benchmark BM-7 – 500K-1750K Degrees if Freedom (DoF) >Intel Xeon E5405 – Dual core runs >Overall speedup up to 3.7X for Single Precision runs, 2.7X for Double Precision

14. Copyright © 2009 Penguin Computing, Inc. All rights reserved Integrated Management Framework  One web based interface to all HPC cluster components  Follows Scyld ‘Ease-of-Use’ Philosophy  Integrates existing tools e.g. IPMI, Ganglia, TORQUE

15. Copyright © 2009 Penguin Computing, Inc. All rights reserved A Sample of our 2500+ Customers National LabsAerospace/Defense Universities/InstitutionsEnterprise

16. Copyright © 2009 Penguin Computing, Inc. All rights reserved 16 Hardware Effects: Multicore-Multithreading  Moore’s Law is doubling the number of transistors on an integrated circuit every 18 months  However, clock speeds are not scaling  Multicore and Multithreaded Programming is critical for continued software scalability  Rather than reinvent the wheel, use existing frameworks and tools >OpenMP >MPI >Threaded Building Blocks >Atlas, FFTW, MKL, AMCL, etc.