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I/O Scheduling Service for Multi-Application Clusters Adrien Lebre Adrien.Lebre@irisa.fr Guillaume Huard, Yves Denneulin { Adrien.Lebre,Guillaume.Huard,Yves.Denneulin } @imag.fr Laboratoire ID-IMAG (UMR 5132), Grenoble, France. BULL - HPC,

  1. I/O Scheduling Service for Multi-Application Clusters Adrien Lebre Adrien.Lebre@irisa.fr Guillaume Huard, Yves Denneulin { Adrien.Lebre,Guillaume.Huard,Yves.Denneulin } @imag.fr Laboratoire ID-IMAG (UMR 5132), Grenoble, France. BULL - HPC, ´ Echirolles, France. Przemyslaw Sowa sowa@icis.pcz.pl Institute of Computer and Information Sciences Czestochowa University of Technology, Poland. Adrien Lebre aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  2. Plan Part 1 - Parallel Input/Output and Clusters Part 2 - Controlling and Scheduling Multi-application I/O Part 3 - aIOLi, an Input/Output Scheduler for HPC Part 4 - Conclusion Adrien Lebre 2/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  3. Plan Part 1 - Parallel Input/Output and Clusters 1 Introduction Context Parallel I/O 2 Parallel I/O and Clusters Available Solutions 3 Objectives Part 2 - Controlling and Scheduling Multi-application I/O Part 3 - aIOLi, an Input/Output Scheduler for HPC Part 4 - Conclusion Adrien Lebre 3/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  4. Context Environment Clusters of SMPs Linux High Performance Computing Intensive I/O applications CPU bounded application ⇒ I/O bounded application Remote hard drive I/O Parallel I/O Handling concurrent accesses to a same resource (a file) Accesses: different in size, in offset Example: a parallel matrix product Adrien Lebre 4/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  5. Parallel I/O - Example Parallel matrix product Specific parts to fetch according to the data distribution (columns/rows, BLOCK/BLOCK ...) Matrix A Matrix B Matrix C a b c ... ... 1 2 3 ... ... ? ? ? ... ... d e f ... ... 4 5 6 ... ... ? ? ? ... ... g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Matrices are stored "row by row" in files Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... Matrix B 1 2 3 ... ... 4 5 6 ... ... 7 8 9 ... ... ... ... ... ... Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  6. Parallel I/O - Example Parallel matrix product Specific parts to fetch according to the data distribution (columns/rows, BLOCK/BLOCK ...) Matrix A Matrix B Matrix C 1 read (n) P 0 n read (1) a b c ... ... 1 2 3 ... ... ? ? ? ... ... d e f ... ... 4 5 6 ... ... ? ? ? ... ... g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Matrices are stored "row by row" in files Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... Matrix B 1 2 3 ... ... 4 5 6 ... ... 7 8 9 ... ... ... ... ... ... Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  7. Parallel I/O - Example Parallel matrix product Specific parts to fetch according to the data distribution (columns/rows, BLOCK/BLOCK ...) Matrix A Matrix B Matrix C 1 read (n) P 0 n read (1) a b c ... ... 1 2 3 ... ... ? ? ? ... ... 1 read (n) d e f ... ... 4 5 6 ... ... ? ? ? ... ... P 1 n read (1) g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Matrices are stored "row by row" in files Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... Matrix B 1 2 3 ... ... 4 5 6 ... ... 7 8 9 ... ... ... ... ... ... Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  8. Parallel I/O - Example Parallel matrix product Specific parts to fetch according to the data distribution (columns/rows, BLOCK/BLOCK ...) File decomposition: Lot of disjoint/contiguous requests at the same time ⇒ ”lethal” behaviour for I/O subsystem Matrix A Matrix B Matrix C 1 read (n) P 0 n read (1) a b c ... ... 1 2 3 ... ... ? ? ? ... ... 1 read (n) d e f ... ... 4 5 6 ... ... ? ? ? ... ... P 1 n read (1) g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... 1 read (n) P i ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... n read (1) Matrices are stored "row by row" in files 1 read (n) P p n read (1) Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... Matrix B 1 2 3 ... ... 4 5 6 ... ... 7 8 9 ... ... ... ... ... ... n2 * read (n) n2 * n read (1) Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  9. Parallel I/O - Example No defined order between requests ⇒ many disk head movements Matrix A Matrix B Matrix C P 0 a b c ... ... 1 2 3 ... ... ? ? ? ... ... d e f ... ... 4 5 6 ... ... ? ? ? ... ... P 1 g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... P i ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Matrix are stored "row by row" in files P p Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... SEEK SEEK SEEK SEEK time Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  10. Parallel I/O - Example No defined order between requests ⇒ many disk head movements Matrix A Matrix B Matrix C P 0 a b c ... ... 1 2 3 ... ... ? ? ? ... ... d e f ... ... 4 5 6 ... ... ? ? ? ... ... P 1 g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... P i ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Matrix are stored "row by row" in files P p Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... SEEK One HDD provides the best performances when files are accessed in a sequential and contiguous way SEEK SEEK SEEK time Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

  11. Parallel I/O - Example No defined order between requests ⇒ many disk head movements Matrix A Matrix B Matrix C P 0 a b c ... ... 1 2 3 ... ... ? ? ? ... ... d e f ... ... 4 5 6 ... ... ? ? ? ... ... P 1 g h i ... ... 7 8 9 ... ... ? ? ? ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... P i ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... Matrix are stored "row by row" in files P p Matrix A a b c ... ... d e f ... ... g h i ... ... ... ... ... ... Matrix B 1 2 3 ... ... 4 5 6 ... ... 7 8 9 ... ... ... ... ... ... Similar behavior for the matrix B ”Random order” between requests The bigger the number of requests the bigger the potential number of seeks ⇒ performance degradation (bottlenecks) Adrien Lebre 5/26 aIOLi - workshop Phenix - December 2006 � Bull-ID LIPS 2006 c

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