SLEEC

SLEEC
Team Members	Purdue U., SNL
PI	Milind Kulkarni (Purdue)
Co-PIs	Arun Prakash (Purdue), Michael Parks (SNL)
Website	https://engineering.purdue.edu/SLEEC
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Semantics-rich Libraries for Effective Exascale Computation or SLEEC

Team Members

• Purdue University: Milind Kulkarni, Arun Prakash, Vijay Pai, Sam Midkiff
• Sandia National Laboratory: Michael Parks

Motivations

• Modern computational science applications composed of many different libraries
• Computational libraries, communication libraries, data structure libraries, etc.
• Peridigm, developed by co-PI Mike Parks, builds on 10 different Trilinos libraries
• Each library has its own idioms and expected usage
• Determining right way to compose and use libraries to solve a problem is difficult

Compositional complexity

• Consider loosely-coupled multi-scale computational mechanics problem (developed by co-PI Arun Prakash)
• Must determine right way to decompose problem, couple separate solutions, etc.

• Simple case: fixed number of subdomains, only consider how to couple them together
• Vast space of configurations: 8 subdomains → 135K possible schedules
• Large variation in performance of different orders
• Exploration of different variants requires knowledge of domain semantics, cost estimates

Difficult interaction between libraries

• Peridigm: computational peridynamics code
  • Allows modeling of materials under stress without explicit accounting for discontinuities (fractures, etc.)
• Built on Trilinos components
  • Set of computation and communication libraries
• Requires careful coordination of data movement operations to manage shadow data, etc. needed by solvers
  • But data movement requirements can be directly inferred from which equations are being solved

Prior Results

• Exploiting library semantics to improve lock placement

• Exploiting library semantics to improve parallelism and locality

Why not compilers

• Compilers do not understand library calls as abstractions
  • Option one: see them as black boxes which give no information → no opportunity for optimization
  • Option two: break abstraction boundaries and try to optimize → many transformation opportunities are only possible by understanding semantics of abstractions

• Needed: a way for compilers to understand abstractions
  • Broadway project attempted this, but focused on analyzing across abstractions, not semantics-driven transformations

Why not domain-specific languages?

• DSLs are a great fit for this
  • Bake abstractions into the language
  • Optimize code at high level of abstraction based on semantic properties
  • Shown to be effective in various domains
    • SPL/Spiral for digital signal processing, Tensor contraction engine, etc.

• But they are not generalizable
  • New domain? New DSL!
  • What about applications that span domains? (e.g., multiphysics codes)

• Needed: a generic infrastructure for incorporating domain knowledge

Project Impact

• [Impact:https://xstackwiki.modelado.org/images/b/b9/SLEEC-Highlight_summary.pdf SLEEC Project Impact]

Principles

• Abstractions carried by domain libraries
  • Domain experts encode semantics, not compiler writers
  • Need effective annotation language for capturing semantics

• Compiler should be domain agnostic
  • Same infrastructure used for optimization and transformation regardless of domain
  • Need common IR for capturing abstractions

• Compiler should be able to optimize for various objectives
  • Do not want to focus solely on performance
  • Need generic optimization ability and cost models

Components

• Annotation language for capturing semantic properties of domain libraries
• High-level intermediate representation to represent programs that use annotated domain libraries
• Transformation strategies that leverage annotations to perform semantics-driven code transformations
• Optimization heuristics that use domain-specific cost models to find more efficient program variants
• Iterative refinement techniques that let the compiler work with incomplete information and infer missing information when possible

Example

• Consider annotated linear algebra library that supports two methods
  • Matrix multiply
  • Equation solve

• Operations have mathematical properties that establish equivalence
  • Can solve ABx = b in two ways:
    • C = AB followed by Cx = b
    • Az = b followed by Bx = z
  • Latter may be more effective if A & B have special properties (e.g., triangular)

Program Code

Abstract

• Abstract into high level representation
• Expression tree to capture flow of data
• Library methods represented as high level operations
• Operands can be subtrees, too, to support composition

Transform

• Transformations expressed as rewrite rules on expression trees
• Rewrites match operation types (domain specific) but compiler applies them without understanding domain semantics

Concretize

• Re-materialize back to source code, or transform to other, lower-level IR

Annotation Language

• Domain libraries annotated by domain experts to interface with compiler infrastructure
• Questions
  • How to abstract libraries into IR
  • What kinds of transformations are legal
    • Represent as rewrite rules
    • How to verify? Can we synthesize?
  • How to concretize
    • Can this be inferred?

Cost models

• Most annotations deal with library interface
  • Semantic properties are associated with library specification, not implementation
• Can also provide cost estimates for library methods
  • Implementation and architecture specific
• Can express other properties of implementation
  • Energy estimates
  • Accuracy information

Compiler Infrastructure

• Compiler does not explicitly understand domains
  • But is extensible, allowing IR to be extended as new domains are added
• Transformations are just pattern-matched rewrite rules
  • Can use domain-specific information such as domain-specific equivalences, domain-specific properties
  • Can also substitute equivalent implementations of same method
• Generic compiler + annotated domain library = domain-specific compiler

Cost-drive optimization

• Applying transformations to program generates semantically equivalent program variants
  • No “best” variant: different implementations will work better in different situations or optimize for different metrics
• Compilation as optimization problem
  • Minimize objective function
    • FLOPs, energy efficiency, etc.
  • Subject to constraints
    • Semantically equivalent to original program, meets accuracy constraints, etc.
• Same infrastructure can be used to optimize for a variety of metrics

Iterative Refinement

• Typical problem with domain-specific languages or annotation approaches: what if program is incompletely annotated?
• Want compiler to still produce useful results
• Key property: compilation process is about optimization, not correctness
  • Lack of information does not raise correctness issues
  • As more annotations are provided, compilation results improve

Inference

• Can we infer missing information?
• Transformation annotations
  • Can we use synthesis techniques to infer legal transformations?
• Cost models
  • Can we use machine learning techniques to build cost models automatically?

Potential Impacts

• Programmability: Programmers can focus on developing methods, using high level libraries, without worrying about careful optimization
• Performance portability: Ability to select between library variants automatically eases transition to new architectures
• Scalability: Cost models can incorporate parallelism, locality, communication to enhance scalability
• Energy efficiency: Parameterized compilation can optimize for energy use instead of performance without rewriting infrastructure
• Resilience: Cost models can incorporate resilience information (e.g., algorithmic fault tolerance information), compilation can choose variants based on resilience properties

Implementation Plan

• Work driven by “challenge” applications and domains
  • Computational mechanics and multiscale techniques (lead: Arun Prakash)
  • Peridynamics and Trilinos libraries (lead: Michael Parks)
• Build compiler infrastructure in ROSE
  • Compiler infrastructure and optimization strategies (leads: Milind Kulkarni and Sam Midkiff)
  • Annotation language and IR (leads: Milind Kulkarni and Sam Midkiff)
  • Cost models and performance modeling (lead: Vijay Pai)

Concrete Deliverables

• Annotation language
• Common IR
• Generic compiler infrastructure
• “Showcase” annotated libraries

Products

Software Releases

• SemCache Will provide annotated, concrete, domain-specific libraries that use SLEEC technology to automatically manage communication between CPU and GPU for codes using Trilinos/Kokkos. Integration in progress.

Presentations/Papers

• "Exploiting Domain Knowledge to Optimize Parallel Computational Mechanics Codes." ICS 2013 (PDF)
• "SemCache: Semantics-aware Caching for Efficient GPU Offloading." ICS 2013. (PDF)
• SLEEC 2014 Progress Presentation (PDF)
• "SemCache++: Semantics-aware Caching for Efficient Multi-GPU Offloading." ICS 2015. (PDF)
• "Exploiting Domain Knowledge to Optimize Mesh Partitioning for Multiscale Methods." Supercomputing 2015 (Poster). (PDF)
• "Optimizing the LULESH Stencil Code using Concurrent Collections." WolfHPC 2015. (PDF)