X-TUNE

X-TUNE
Team Members	U. of Utah, ANL, LBNL, USC/ISI
PI	Mary Hall (U. of Utah)
Co-PIs	Paul Hovland (ANL), Samuel Williams (LBNL), Jacqueline Chame (USC/ISI)
Website	http://ctop.cs.utah.edu/x-tune/
Download	{{{download}}}

Autotuning for Exascale or X-TUNE - Self-Tuning Software to manage Heterogeneity

Team Members

• University of Utah: Mary Hall (Lead PI)
• Argonne National Laboratory (ANL): Paul Hovland, Stefan Wild, Krishna Narayanan, Jeff Hammond
• Lawrence Berkeley National Laboratory (LBNL): Sam Williams, Lenny Oliker, Brian van Straalen
• University of Southern California: Information Science Institute (USC/ISI): Jacqueline Chame

What is Autotuning?

Definition:

• Automatically generate a “search space” of possible implementations of a computation
  • A code variant represents a unique implementation of a computation, among many
  • A parameter represents a discrete set of values that govern code generation or execution of a variant
• Measure execution time and compare
• Select the best-performing implementation (for exascale, tradeoff between performance/energy/reliability)

Key Issues:

• Identifying the search space
• Pruning the search space to manage costs
• Off-line vs. on-line search

Three Types of Autotuning Systems

• Autotuning libraries
  • Library that encapsulates knowledge of its performance under different execution environments
  • Dense linear algebra: ATLAS, PhiPAC
  • Sparse linear algebra: OSKI
  • Signal processing: SPIRAL, FFTW

• Application-specific autotuning
  • Active Harmony provides parallel rank order search for tunable parameters and variants
  • Sequoia and PetaBricks provide language mechanism for expressing tunable parameters and variants

• Compiler-based autotuning
  • Other examples: Saday et al., Swany et al., Eignenmann et al.
  • Related concepts: iterative compilation, learning-based compilation

X-TUNE Goals

A unified autotuning framework that seamlessly integrates programmer-directed and compiler-directed autotuning

• Expert programmer and compiler work collaboratively to tune a code
  • Unlike previous systems that place the burden on either programmer or compiler
  • Provides access to compiler optimizations, offering expert programmers the control over optimization they so often desire

• Design autotuning to be encapsulated in domain-specific tools
  • Enables less-sophisticated users of the software to reap the benefit of the expert programmers’ efforts

• Focus on Adaptive Mesh Refinement Multigrid (Combustion Co-Design Center, BoxLib, Chombo) and tensor contractions (TCE)

X-TUNE Structure

Autotuning Language Extensions

• Tunable Variables
  • An annotation on the type of a variable (as in Sequoia)
  • Additionally, specify range, constraints and a default value

• Computation Variants
  • An annotation on the type of a function (as in PetaBricks)
  • Additionally, specify (partial) selection criteria
  • Multiple variants may be composed in the same execution

Separate mapping description captures architecture-specific aspects of autotuning.

Compiler-Based Autotuning

• Foundational Concepts
  • Identify search space through a high-level description that captures a large space of possible implementations
  • Prune space through compiler domain knowledge and architecture features
  • Provide access to programmers with transformation recipes, or recipes generated automatically by compiler decision algorithm
  • Uses source-to-source transformation for portability, and to leverage vendor code generation
  • Requires restructuring of the compiler

CHiLL Implementation

Transformation Recipes for Autotuning

Incorporate the Best Ideas from Manual Tuning

Compiler + Autotuning can yield comparable and even better performance than manually-tuned libraries

Pbound: Performance Modeling for Autotuning

• Performance modeling increases the automation in autotuning
  • Manual transformation recipe generation is tedious and error-prone
  • Implicit models are not portable across platforms

• Models can unify programmer guidance and compiler analysis
  • Programmer can invoke integrated models to guide autotuning from application code
  • Compiler can invoke models during decision algorithms

• Models optimize autotuning search
  • Identify starting points
  • Prune search space to focus on most promising solutions
  • Provide feedback from updates in response to code modifications

Reuse Distance and Cache Miss Prediction

Reuse distance

• For regular (affine) array references
  • Compute reuse distance, to predict data footprints in memory hierarchy
  • Guides transformation and data placement decisions

Cache miss prediction

• Use to predict misses
• Assuming fully associative cache with n lines (optimistic case), a reference will hit if the reuse distance d<n

Application Signatures + Architecture

How will modeling be used?

• Single-core and multicore models for application performance will combine architectural information, user-guidance, and application analysis
• Models will be coupled with decision algorithms to automatically generate CHiLL transformation recipes
  • Input: Reuse Information, Loop Information etc.
  • Output: Set of transformation scripts to be used by empirical search
• Feedback to be used to refine model parameters and behavior
• Small and large application execution times will be considered

Example: Stencils and Multigrid

• Stencil performance bound, when bandwidth limited:

 Performance (gflops) <= stencil flops * STREAM bandwidth / grid size

• Multigrid solves Au=f by calculating a number of corrections to an initial solution at varying grid coarsenings (“V-cycle”)
  • Each level in the v-cycle: perform 1-4 relaxes (~stencil sweeps)
  • Repeat multiple v-cycles reducing the norm of the residual by an order of magnitude each cycle

Multigrid and Adaptive Mesh Refinement

File:XTUNE-Multigrid.png

• Some regions of the domain may require finer fidelity than others
• In Adaptive Mesh Refinement, we refine those regions to a higher resolution in time and space
• Typically, one performs a multigrid “level solve” for one level (green, blue, red) at a time
• Coarse-fine boundaries (neighboring points can be at different resolutions) complicate the calculation of the RHS and ghost zones for the level
• Each level is a collection of small (32³ or 64³) boxes to minimize unnecessary work
• These boxes will be distributed across the machine for load balancing (neighbors are not obvious/implicit)

Autotuning for AMR Multigrid

• Focus is addressing data movement, multifaceted:
  • Automate fusion of stencils within an operator. Doing so may entail aggregation of communication (deeper ghost zones)
  • Extend and automate the communication-avoiding techniques developed in CACHE
  • Automate application of data movement-friendly coarse-fine boundary conditions
  • Automate hierarchical parallelism within a node to AMR MG codes
  • Explore alternate data structures
  • Explore alternate stencil algorithms (higher order, …)
• Proxy architectures: MIC, BG/Q, GPUs
• Encapsulate into an embedded DSL approach

Summary and Leverage

• Build integrated end-to-end autotuning, focused on AMR multigrid and tensor contractions
  • Language and compiler guidance of autotuning
  • Programmer and compiler collaborate to tune a code
  • Modeling assists programmer, compiler writer, and search space pruning

• Leverage and integrate with other X-Stack teams
  • Our compiler technology all based on ROSE so can leverage from and provide capability to ROSE
  • Domain-specific technology to facilitate encapsulating our autotuning strategies
  • Collaborate with MIT on autotuning interface
  • Common run-time for a variety of platforms (e.g., GPUs and MIC), and supports a large number of potentially hierarchical threads