Enterprise AI Analysis
Automated Workflow for Floating-Point Analysis of GPU Kernels
Explore a novel, automated approach leveraging open-source tools to analyze floating-point error in SYCL kernels, enhancing numerical stability and performance in HPC applications.
Executive Impact: Precision and Performance in HPC
Our groundbreaking workflow introduces an automated solution for floating-point error analysis in GPU kernels, addressing a critical need in HPC. By integrating OpenCL interception, CPU-based replay via PoCL and Verificarlo, we enable comprehensive evaluation of numerical stability and reduced-precision scenarios without modifying original applications. This approach significantly reduces analysis overhead and provides actionable insights for developers, ensuring robustness across diverse hardware.
Performance Improvement (PoCL over Intel CPU Runtime)
Overhead Range (Verificarlo instrumentation)
Significant Digits Retained (PRISM backend, most particles)
Deep Analysis & Enterprise Applications
Our deep analysis reveals key findings across performance, numerical stability, and the potential of reduced precision, demonstrating the workflow's versatility and impact on scientific computing.
Examine the runtime overheads and performance characteristics of different OpenCL runtimes and Verificarlo configurations. Understand the trade-offs between precision and execution speed, identifying bottlenecks and optimization opportunities.
Dive into the numerical stability of the HACC force kernel using Monte Carlo Arithmetic (MCA) and Probabilistic Rounding with Instruction Set Management (PRISM). Discover how rounding errors propagate and impact the accuracy of particle velocity calculations, highlighting sensitive components.
Investigate the feasibility of using reduced-precision floating-point types (e.g., FP16, TF32, BF16) to leverage modern hardware capabilities. Analyze the error introduced by converting inputs and simulating computations with lower precision, balancing performance gains with acceptable accuracy loss.
Understand the technical complexities involved in building an automated workflow for floating-point analysis. Explore challenges related to compiler optimizations, work-group size limitations, and the necessity of domain-specific knowledge for accurate interpretation of results.
Max ULP Difference (Intel CPU Runtime, X-component)
Enterprise Process Flow
Compile SYCL to SPIR-V
→
Run on GPU with Intercept Layer
→
Replay on CPU with PoCL & Verificarlo
→
Analyze Floating-Point Behavior
| Feature | IEEE-Compliant (00) | Fast-Math (03) |
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| Numerical Stability (Sig. Digits) |
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| Performance |
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| Compiler Optimizations |
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HACC Kernel Analysis: Uncovering Numerical Nuances
Challenge: Analyzing the floating-point behavior of the short-range force kernel in HACC, a large-scale cosmology application, across diverse hardware and precision settings without modifying the original SYCL codebase. Identifying sources of numerical instability and validating reduced-precision suitability.
Solution: Our automated workflow leveraged OpenCL interception to capture GPU kernel executions, then replayed them on a CPU using PoCL and Verificarlo. This allowed for systematic evaluation of IEEE compliance, MCA, PRISM, and reduced-precision modes (FP16, TF32, BF16). Offline input analysis further guided precision choices.
Impact: The analysis provided confidence in HACC's numerical stability even with 'fast-math' optimizations and identified potential for adopting reduced-precision types for performance gains. It also highlighted workflow challenges, informing future development towards full automation and broader adoption in HPC.
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