1. 1/30/2003 BARC1 Profile-Guided I/O Partitioning Yijian Wang David Kaeli Electrical and Computer Engineering Department Northeastern University {yiwang, kaeli}@ece.neu.edu

2. 1/30/2003 BARC2 Outline Introduction Related work Profile-guided I/O partitioning Benchmarks Experimental results Conclusions and future work

3. 1/30/2003 BARC3 Introduction The I/O bottleneck –The growing gap between the speed of processors and I/O devices –Some applications access disks very frequently I/O intensive applications –Multimedia applications –Database applications –Parallel scientific applications

4. 1/30/2003 BARC4 Related work Fast disks –FC-connected SCSI disks –Smart caching I/O controller (EMC, IO Integrity) Parallel I/O –Parallel disks (i.e., RAID) –Parallel file systems (NFS, PIOF, HPS, etc.) –Runtime parallel systems (MPI-IO, ROMIO, ADIO) –Compiler technology (Loop tiling, compiler-directed collective I/O) –To achieve high performance, I/O should be parallelized at multiple levels (application, file system, disks)

5. 1/30/2003 BARC5 I/O Partitioning Our target applications are parallel scientific codes running on Beowulf clusters I/O is parallelized at both the application level (using MPI and MPI-IO) and the disk level (using file partitioning) Ideally, every process will only access files on local disk (though this is typically not possible due to data sharing) How to recognize the access patterns ? dynamically (profiling) statically (compiler)

6. 1/30/2003 BARC6 Profile generation Run the application Capture I/O traces Apply our partitioning algorithm Rerun the tuned application

7. 1/30/2003 BARC7 I/O traces and partitioning For every process, for every contiguous file access, we capture the following I/O profile information: –Process ID –File ID –Address –Chunk size –I/O operation (read/write) –Timestamp Generate a partition for every process Partitioning is NP-complete

8. 1/30/2003 BARC8 For each MPI-IO process create a file partition; For each contiguous data chunk identify the process that most frequently accesses this chunk; assign the chunk to the associated partition; For each partition reorder data in the partition based on first access to each chunk; Our Greedy Algorithm

9. 1/30/2003 BARC9 Benchmarks NASA Parallel Benchmark (NPB2.4)/BT –Computational fluid dynamics –Generates a file (~1.6 GB) dynamically and then reads it –Writes/reads sequentially in chunk sizes of 2040 Bytes SPEChpc96/seismic –Seismic processing –Generates a file (~1.5 GB) dynamically and then reads it back –Writes sequential chunks of 96 KB and reads sequential chunks of 2 KB mpi-tile-io –Parallel Benchmarking Consortium –Tile access to a two-dimensional matrix (~1 GB) with overlap –Writes/reads sequentially chunks of 32 KB, with 2KB of overlap All applications uses MPI and MPI-IO for computation, communication and I/O

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16. 1/30/2003 BARC16 Conclusions and future work We obtain scalable speedup due to: creating parallel I/O channels reducing disk seek time reducing communication overhead I/O access patterns are generally independent of data values, for the applications studied Investigating static (compile time) approaches to I/O partitioning

17. 1/30/2003 BARC17 Northeastern University Computer Architecture Research Group http://www.ece.neu.edu/groups/nucar This project is supported by the NSF-funded Center for Subsurface Sensing and Imaging System (CenSSIS)