DEGAS

DEGAS
Team Members	LBNL, Rice U., UC Berkeley, UT Austin, LLNL, NCSU
PI	Katherine Yelick
Co-PIs	Vivek Sarkar (Rice U.), James Demmel (UC Berkeley), Mattan Erez (UT Austin), Dan Quinlan (LLNL)
Website	DEGAS
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Dynamic Exascale Global Address Space or DEGAS

Team Members

• Lawrence Berkeley National Laboratory (LBNL)
• Rice University
• University of California, Berkeley
• University of Texas at Austin
• Lawrence Livermore National Laboratory (LLNL)
• North Carolina State University (NCSU)

Project Impact

• DEGAS Project Impact

Mission

Mission Statement: To ensure the broad success of Exascale systems through a unified programming model that is productive, scalable, portable, and interoperable, and meets the unique Exascale demands of energy efficiency and resilience.

Goals & Objectives

• Scalability: Billion‐way concurrency, thousand‐way on chip with new architectures
• Programmability: Convenient programming through a global address space and high‐level abstractions for parallelism, data movement and resilience
• Performance Portability: Ensure applications can be moved across diverse machines using implicit (automatic) compiler optimizations and runtime adaptation
• Resilience: Integrated language support for capturing state and recovering from faults
• Energy Efficiency: Avoid communication, which will dominate energy costs, and adapt to performance heterogeneity due to system-­‐level energy management
• Interoperability: Encourage use of languages and features through incremental adoption

Programming Models

Two Distinct Parallel Programming Questions

• What is the parallel control model?

• What is the model for sharing/communication?

Applications Drive New Programming Models

• Message Passing Programming
  • Divide up domain in pieces
  • Compute one piece and exchange
  • MPI and many libraries

• Global Address Space Programming
  • Each start computing
  • Grab whatever/whenever
  • UPC, CAF, X10, Chapel, Fortress, Titanium, GlobalArrays

Hierarchical Programming Model

• Goal: Programmability of exascale applications while providing scalability, locality, energy efficiency, resilience, and portability
  • Implicit constructs: parallel multidimensional loops, global distributed data structures, adaptation for performance heterogeneity
  • Explicit constructs: asynchronous tasks, phaser synchronization, locality

• Built on scalability, performance, and asynchrony of PGAS models
  • Language experience from UPC, Habanero‐C, Co‐Array Fortran, Titanium

• Both intra and inter‐node; focus is on node model

• Languages demonstrate DEGAS programming model
  • Habanero‐UPC: Habanero’s intra‐node model with UPC’s inter‐node model
  • Hierarchical Co‐Array Fortran (CAF): CAF for on‐chip scaling and more
  • Exploration of high level languages: E.g., Python extended with H‐PGAS

• Language‐independent H‐PGAS Features:
  • Hierarchical distributed arrays, asynchronous tasks, and compiler specialization for hybrid (task/loop) parallelism and heterogeneity
  • Semantic guarantees for deadlock avoidance, determinism, etc.
  • Asynchronous collectives, function shipping, and hierarchical places
  • End‐to‐end support for asynchrony (messaging, tasking, bandwidth utilization through concurrency)
  • Early concept exploration for applications and benchmarks

Communication-Avoiding Compilers

• Goal: massive parallelism, deep memory and network hierarchies, plus functional and performance heterogeneity
  • Fine‐grained task and data parallelism: enable performance portability
  • Heterogeneity: guided by functional, energy and performance characteristics
  • Energy efficiency: minimize data movement and hooks to runtime adaptation
  • Programmability: manage details of memory, heterogeneity, and containment
  • Scalability: communication and synchronization hiding through asynchrony

• H-PGAS into the Node
  • Communication is all data movement

• Build on code‐generation infrastructure
  • ROSE for H‐CAF and Communication‐Avoidance optimizations
  • BUPC and Habanero‐C; Zoltan
  • Additional theory of CA code generation

Exascale Programming: Support for Future Algorithms

• Approach: “Rethink” algorithms to optimize for data movement
  • New class of communication‐optimal algorithms
  • Most codes are not bandwidth limited, but many should be

• Challenges: How general are these algorithms?
  • Can they be automated and for what types of loops?
  • How much benefit is there in practice?

Adaptive Runtime Systems (ARTS)

• Goal: Adaptive runtime for manycore systems that are hierarchical, heterogeneous and provide asymmetric performance
  • Reactive and proactive control: for utilization and energy efficiency
  • Integrated tasking and communication: for hybrid programming
  • Sharing of hardware threads: required for library interoperability

• Novelty: Scalable control; integrated tasking with communication
  • Adaptation: Runtime annotated with performance history/intentions
  • Performance models: Guide runtime optimizations, specialization
  • Hierarchical: Resource/energy
  • Tunable control: Locality/load balance

• Leverages: Existing runtimes
  • Lithe scheduler composition; Juggle
  • BUPC and Habanero‐C runtimes

Synchronization Avoidance vs Resource Management

• Management of critical resources will be more important:
  • Memory and network bandwidth limited by cost and energy
  • Capacity limited at many levels: network buffers at interfaces, internal network congestion are real and growing problems

• Can runtimes manage these or do users need to help?
  • Adaptation based on history and (user‐supplied) intent?
  • Where will bottlenecks be for a given architecture and application?

Lith Scheduling Abstraction: "Harts" (Hardware Threads)

Lightweight Communication (GASNet-EX)

• Goal: Maximize bandwidth use with lightweight communication
  • One‐sided communication: to avoid over‐synchronization
  • Active‐Messages: for productivity and portability
  • Interoperability: with MPI and threading layers

• Novelty:
  • Congestion management: for 1‐sided communication with ARTS
  • Hierarchical: communication management for H‐PGAS
  • Resilience: globally consist states and fine‐grained fault recovery
  • Progress: new models for scalability and interoperatbility

• Leverage GASNet (redesigned):
  • Major changes for on‐chip interconnects
  • Each network has unique opportunities

Resilience through Containment Domains

• Goal: Provide a resilient runtime for PGAS applications
  • Applications should be able to customize resilience to their needs
  • Resilient runtime that provides easy‐to‐use mechanisms

• Novelty: Single analyzable abstraction for resilience
  • PGAS Resilience consistency model
  • Directed and hierarchical preservation
  • Global or localized recovery
  • Algorithm and system‐specific detection, elision, and recovery

• Leverage: Combined superset of prior approaches
  • Fast checkpoints for large bulk updates
  • Journal for small frequent updates
  • Hierarchical checkpoint‐restart
  • OS‐level save and restore
  • Distributed recovery

Resilience: Research Questions

1. How to define consistent (i.e. allowable) states in the PGAS model?

• Theory well understood for fail‐stop message‐passing, but not PGAS.

2. How do we discover consistent states once we've defined them?

• Containment domains offer a new approach, beyond conventional sync-and‐stop algorithms.

3. How do we reconstruct consistent states after a failure?

• Explore low overhead techniques that minimize effort required by applications programmers.
• Leverage BLCR, GASnet, Berkeley UPC for development, and use Containment Domains as prototype API for requirements discovery

Energy and Performance Feedback

• Goal: Monitoring and feedback of performance and energy for online and offline optimization
  • Collect and distill: performance/energy/timing data
  • Identify and report bottlenecks: through summarization/visualization
  • Provide mechanisms: for autonomous runtime adaptation

• Novelty: Automated runtime introspection
  • Provide monitoring: power/network utilization
  • Machine Learning: identify common characteristics
  • Resource management: including dark silicon

• Leverage: Performance/energy counters
  • Integrated Performance Monitoring (IPM)
  • Roofline formalism
  • Performance/energy counters

Software Stack

DEGAS Pieces of the Puzzle