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Enhancing MPI and Parallel Code Support

This project focuses on improving end-user documentation, quality-controlled MPI site deployment, and engagement through outreach and participation in standardization bodies. It highlights MPI application libraries, site reliability, performance testing, OpenMP and GPGPU support, GPU resource scheduling, and GPU scheduling solutions in a mixed Grid/Cloud environment. The project emphasizes the need for wider deployment of UMD WMS 3.3 for improved generic parallel job support, and the exciting advancements with GPGPU and virtualization.

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Enhancing MPI and Parallel Code Support

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  1. MPI and Parallel Code Support Alessandro Costantini, Isabel Campos, Enol Fernández, Antonio Laganà, John Walsh

  2. Core Objectives • Improved end-user documentation, addressing MPI application development and job submission in ARC, gLite and UNICORE, • Quality controlled MPI site deployment documentation, • Outreach and dissemination at major EGI events and workshops, • User community, NGI and site engagement, gathering direct input, • Participation in selected standardisation bodies.

  3. MPI Application Libraries • Most Common Application Libraries • DL_POLY • NAMD • VENUS96 • GROMACS • GAMESS • MUFTE • mpiBLAST • Some app libs tightly tied to specific MPI libraries (and may need local re-compile)

  4. MPI Status • 119 clusters publish MPI-START tag • Very little change since last year • However, big change in reliability! • Sites now tested every hour via SAM • NGIs/Sites must follow-up on MPI failures • CompChem VO performed wide scale testing • Uses larger number of cpu cores than SAM. • Uses UNIPG production codes of DL_POLY • 16 sites of 25 support both CompChem and MPI • Tested sequentially on one node, then parallel on 2, 4, 8, 16, 32, 64 nodes

  5. Performances over the Grid • - SAM-MPI tests enabled • Parallel applications run properly on 12 sites up to 16 CPUs • Clearly a need to isolate outstanding issues!

  6. Success rates and issues 2 to 8 Cores 16 to 64 Cores

  7. OpenMP/User defined allocation support • New M/W features requested by users • OpenMP support added to MPI-START • User defined allocation of processes/node • Accounting issues being investigated • Expected release in UMD 1.3 • OpenMP advantages • Most sites now use >= 4 cores per machine • OpenMP is lightweight, easy to use, quick

  8. GPGPU support • GPGPU now a commodity product • High-end units offer ECC/better precision • CUDA/OpenCL features improving • Especially double precision calculations • Large scale growth at HPC centres • As seen in HPC Top 500 • Large set of Applications • Across all scientific domains • OpenMPI support (must be “compiled in”)

  9. GPGPU - Caveat Emptor • CUDA/OpenCL has steep learning curve • All user a/c have R/W access to resource • Most nodes now MultiCore • N job slots per physical machine • Distinct pool a/c may try to access same GPU • User code needs guaranteed exclusive access • GPGPU resource schedulers • Basic support in Torque 2.5.4 • No support in MAUI (MOAB yes) • SLURM supports GPGPU resources

  10. Current GPU Scheduling Solutions • Innovative mixed Grid/Cloud approach • Need Grid standardisation (#GPU cores etc) • Exploits new features in: • WMS + H/W virtualization + PCI pass-through of GPU to VM • Single CPU job slot to handle LRMS GPU resource allocation • Used by CompChem and TheoPhys • VOs may (in the future ) be able to deploy own MPI apps as VMs

  11. Conclusions • SAM-MPI tests solve usual site problems • Easier to detect source of failure • MPI now more reliable for large jobs • Waiting times at sites can be prohibitive • Works best when as many free nodes at site as job size. • Need wider deployment of UMD WMS 3.3 • Improved generic parallel job support • Exciting time ahead with GPGPU/Virtualisation

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