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Global Trees: A Framework for Linked Data Structures on Distributed Memory Parallel Systems

Global Trees: A Framework for Linked Data Structures on Distributed Memory Parallel Systems. D. Brian Larkins, James Dinan, Sriram Krishnamoorthy, Srinivasan Parthasarthy, Atanas Rountev, P. Sadayappan. Background. Trees and graphs can concisely represent relationships between data

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Global Trees: A Framework for Linked Data Structures on Distributed Memory Parallel Systems

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  1. Global Trees: A Framework for Linked Data Structures on Distributed Memory Parallel Systems • D. Brian Larkins, James Dinan, Sriram Krishnamoorthy, Srinivasan Parthasarthy, Atanas Rountev, P. Sadayappan

  2. Background • Trees and graphs can concisely represent relationships between data • Data sets are becoming increasingly large and can require compute-intensive processing • Developing efficient, memory hierarchy-aware applications is hard

  3. Sample Applications • n-body simulation • Fast Multipole Methods (FMM) • multiresolution analysis • clustering and classification • frequent pattern mining

  4. Key Contributions • Efficient fine-grained data access with a global view of data • Exploit linked structure to provide fast global pointer dereferencing • High-level, locality-aware, parallel operations on linked data structures • Application-driven customization • Empirical validation of the approach

  5. Framework Design

  6. Global Chunk Layer (GCL) • API and run-time library for managing chunks - built on ARMCI • Abstracts common functionality for handling irregular, linked data • Provides a global namespace with access and modification operations • Extensible and highly customizable to maximize functionality and performance

  7. Chunks • A chunk is: • Contiguous memory segment • Globally accessible • Physically local to only one process • Collection of user-defined elements • Unit of data transfer

  8. Programming Model • SPMD with MIMD-style parallelism • Global pointers permit fine-grained access • Chunks allow coarse-grained data movement • Uses get/compute/put model for globally shared data access • Provides both uniform global view and chunked global view of data

  9. Global Pointers p c } } c = &p.child[i] + p.child[i].ci + p.child[i].no 4252 + -4252 + 4340

  10. Global Trees (GT) • Run-time library and API for global view programming trees on DM clusters • Built on GCL chunk communication framework • High-level tree operations which work in parallel and are locality aware • Each process can asynchronously access any portion of the shared tree structure

  11. GT Concepts • Tree Groups • set of global trees • allocations are made from the same chunk pool • Global Node Pointers • Tree Nodes • link structure managed by GT • body is user-defined structure

  12. Example: Copying a Tree

  13. Tree Traversals • GT provides optimized, parallel traversals for common traversal orders • Visitor callbacks are application-defined computations on a single node • GT currently provides top-down, bottom-up, and level-wise traversals

  14. Sample Traversal Usage

  15. Node Mapping

  16. Custom Allocation • No single mapping of data elements to chunks will be optimal • GT/GCL supports custom allocators to improve spatial locality • Allocators can use a hint from call-site and can keep state between calls • Default allocation is local-open

  17. Experimental Results • Evaluate using: • Barnes-Hut from SPLASH-2 • Compression operation from MADNESS • GT compared with: • Intel’s Cluster OpenMP and TreadMarks runtime • UPC

  18. Global Pointer Overhead compress() Barnes-Hut

  19. Chunk Size and Bandwidth Experiments run on the department WCI Cluster - 2.33GHz Intel Xeon, 6GB RAM, Infiniband

  20. Impact of Chunk UtilizationBarnes-Hut Experiments run on the department WCI Cluster - 2.33GHz Intel Xeon, 6GB RAM, Infiniband

  21. Barnes-Hut Chunk Size Selection Barnes-Hut application from SPLASH-2 suite

  22. Barnes-Hut Scaling chunk size = 256, bodies = 512k

  23. Local vs. Remote AccessMADNESS compress()

  24. Related Approaches • Distributed Shared Memory (DSM) • Cluster OpenMP, TreadMarks, Cashmere, Midway • Distributed Shared Objects (DSO) • Charm++, Linda, Orca • Partitioned Global Address Space (PGAS) Languages and Systems • UPC, Titanium, CAF, ARMCI, SHMEM, GASNET • Shared pointer-based data structure support on distributed memory clusters • Parallel Irregular Trees, Olden • HPCS Programming Languages • Chapel, X10

  25. Future Work • Global Graphs • GT data reference locality tools • More applications

  26. Questions email: larkins@cse.ohio-state.edu

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