CORVETTE

CORVETTE
Team Members	UC Berkeley, LBNL
PI	Koushik Sen (UC Berkeley)
Co-PIs	James W. Demmel (UC Berkeley), Costin Iancu (LBNL)
Website	CORVETTE
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Program Correctness, Verification, and Testing for Exascale or CORVETTE

Team Members

Researchers

• Koushik Sen
• James Demmel
• Costin Iancu

Postdoctoral Researchers

• Hong Diep Nguyen
• Xuehai Qian
• Cindy Rubio-González

Graduate Student Researcher

• Cuong Nguyen

Motivation

• High performance scientific computing
  • Exascale: O(10⁶) nodes, O(10³) cores per node
  • Requires asynchrony and “relaxed” memory consistency
  • Shared memory with dynamic task parallelism
  • Languages allow remote memory modification

• Correctness challenges
  • Non-deterministic causes hard to diagnose correctness and performance bugs
    • Data races, atomicity violations, deadlocks …
  • Bugs in DSL
  • Scientific applications use floating-points: non-determinism leads to non-reproducible results
  • Numerical exceptions can cause rare but critical bugs that are hard for non-experts to detect and fix

Goals

Develop correctness tools for different programming models: PGAS, MPI, dynamic parallelism

I. Testing and Verification

• Identify sources of non-determinism in executions
• Data races, atomicity violations, non‐reproducible floating point results
• Explore state-of-the-art techniques that use dynamic analysis
• Develop precise and scalable tools: < 2X overhead

II. Debugging

• Use minimal amount of concurrency to reproduce bug
• Support two-level debugging of high-level abstractions
• Detect causes of floating-point anomalies and determine the minimum precision needed to fix them

Publications

• Fast Reproducible Floating-Point Summation. J. Demmel and H.D. Nguyen. In Proceedings of the 21st IEEE Symposium on Computer Arithmetic (ARITH'13), Austin, TX, April 2013.
• Numerical Accuracy and Reproducibility at ExaScale. J. Demmel and H.D. Nguyen. In Proceedings of the 21st IEEE Symposium on Computer Arithmetic (ARITH'13), Austin, TX, April 2013.
• Scaling Data Race Detection for Partitioned Global Address Space Programs. C. Park, K. Sen, C. Iancu. In Proceedings of the International Conference on Supercomputing (ICS'13), Eugene, OR, June 2013.
• Precimonious: Tuning Assistant for Floating-Point Precision. C. Rubio-González, C. Nguyen, H.D. Nguyen, J. Demmel, W. Kahan, K. Sen, D.H. Bailey, C. Iancu, and D. Hough. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'13), Denver, CO, November 2013.
• Parallel Reproducible Summation. J. Demmel and H.D. Nguyen. To appear in IEEE Transaction on Computer, Special Section on Computer Arithmetic 2014.

Testing and Verification Tools

Scalable Testing of Parallel Programs

• Concurrent Programming is hard
  • Bugs happen non­‐deterministically
  • Data races, deadlocks, atomicity violations, etc.

• Goals: build a tool to test and debug concurrent and parallel programs
  • Efficient: reduce overhead from 10x‐100x to 2x
  • Precise
  • Reproducible
  • Scalable

• Active random testing

Active Testing

• Phase 1: Static or dynamic analysis to find potential concurrency bug patterns, such as data races, deadlocks, atomicity violations
  • Data races: Eraser or lockset based [PLDI’08]
  • Atomicity violations: cycle in transactions and happens‐before relation [FSE’08]
  • Deadlocks: cycle in resource acquisition graph [PLDI’09]
  • Publicly available tool for Java/Pthreads/UPC [CAV’09]
  • Memory model bugs: cycle in happens­‐before graph [ISSTA’11]
  • For UPC programs running on thousands of cores [SC’11]

• Phase 2: “Direct” testing (or model checking) based on the bug patterns obtained from phase 1
  • Confirm bugs

Goals

• Goal 1. Nice to have a trace exhibiting the data race
• Goal 2. Nice to have a trace exhibiting the assertion failure
• Goal 3. Nice to have a trace with fewer threads
• Goal 4. Nice to have a trace with fewer context switches

Challenges for Exascale

• Java and pthreads programs
  • Synchronization with locks and condition variables
  • Single node
• Exascale has different programming models
  • Large scale
  • Bulk communication
  • Collective operations with data movement
  • Memory consistency
  • Distributed shared memory
• Cannot use centralized dynamic analyses
• Cannot instrument and track every statement

Further Challenges

• Targeted a simple programming paradigm
  • Threads and shared memory
• Similar techniques are available for MPI and CUDA
  • ISP, DAMPI, MARMOT, Umpire, MessageChecker
  • TASS uses symbolic execu7on
  • PUG for CUDA
• Analyze programs that mix different paradigms
  • OpenMP, MPI, Shared Distributed Memory
  • Need to correlate non‐determinism across paradigms

How Well Does it Scale?

• Maximum 8% slowdown at 8K cores
  • Franklin Cray XT4 Supercomputer at NERSC
  • Quad­‐core 2.x3GHz CPU and 8GB RAM per node
  • Portals interconnect
• Optimizations for scalability
  • Efficient Data Structures
  • Minimize Communication
  • Sampling with Exponential Backoff

Debugging Tools

Debugging Project I

• Detect bug with fewer threads and fewer context switches

Experience with C/PThreads: Over 90% of simplified traces were within 2 context switches of optimal

Small model hypothesis for parallel programs

• Most bugs can be found with few threads
  • 2‐3 threads
  • No need to run on thousands of nodes
• Most bugs can be found with fewer context switches [Musuvathi and Qadeer, PLDI 07]
  • Helps in sequential debugging

Debugging Project II

• Two‐level debugging of DSLs
• Correlate program state across program versions

Debugging Project III

• Find floating point anomalies
• Recommend safe reduction of precision

Floating Point Debugging

Why do we care?

• Usage of floating point programs has been growing rapidly
  • HPC
  • Cloud, games, graphics, finance, speech, signal processing

• Most programmers are not expert in floating‐point!
  • Why not use highest precision everywhere

• High precision wastes
  • Energy
  • Time
  • Storage

Floating Point Debugging Problem 1: Reduce unnecessary precision

• Consider the problem of finding the arc length of the function

How can we find a minimal set of code fragments whose precision must be high?

Floating Point Debugging Problem 2: Detect Inaccuracy and Anomaly

What can we do?

• We can reduce precision “safely”
  • reduce power, improve performance, get better answer

• Automated testing and debugging techniques
  • To recommend “precision reduction”
  • Formal proof of “safety” can be replaced by Concolic testing

• Approach: automate previously hand-made debugging
  • Concolic testing
  • Delta debugging [Zeller et al.]

Implementation

• Prototype implementation for C programs
  • Uses CIL compiler framework
  • http://perso.univ-perp.fr/guillaume.revy/index.php?page=debugging

• Future plans
  • Build on top of LLVM compiler framework

Potential Collaboration

• Dynamic analyses to find bugs ‐ dynamic parallelism, unstructured parallelism, shared memory
  • DEGAS, XPRESS, Traleika Glacier

• Floating point debugging
  • Co‐design centers

• 2‐level debugging
  • D-TEC

Summary

• Build testing tools
  • Close to what programmers use
  • Hide formal methods and program analysis under testing

• If you are not obsessed with formal correctness
  • Testing and debugging can help you solve these problems with high confidence