A5067135457- Parallel Computing and Optimization Techniques
- Cloud Computing and Resource Management
- Distributed and Parallel Computing Systems
- Nuclear Physics and Applications
- Scientific Computing and Data Management
- Nuclear Engineering Thermal-Hydraulics
- Embedded Systems Design Techniques
- Advanced Data Storage Technologies
- Nuclear reactor physics and engineering
Princeton University
2022-2024
Memory profiling captures programs’ dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique program trace summary, various profiler types have been developed. Yet, designing practical profilers often requires extensive expertise, adeptness optimization, significant implementation effort. This results a void where aspirations for fast robust remain...
Manually writing parallel programs is difficult and error-prone. Automatic parallelization could address this issue, but profitability can be limited by not having facts known only to the programmer. A parallelizing compiler that collaborates with programmer increase coverage performance of while reducing errors overhead associated manual parallelization. Unlike collaboration involving analysis tools report program properties or make suggestions programmer, decompiler-based leverage strength...
This paper presents a modeling methodology and framework for the lifetime reliability evaluation of heterogeneous accelerator systems. Architectural heterogeneity has been advocated as design strategy to enhance performance energy efficiency computing With various types processing units (PUs) deployed in system, workload is executed using multiple PUs. However, designs raise concerns since different PUs have distinct roles thus are irreplaceable. diverse failure mechanisms distributions, it...
Memory profiling captures programs' dynamic memory behavior, assisting programmers in debugging, tuning, and enabling advanced compiler optimizations like speculation-based automatic parallelization. As each use case demands its unique program trace summary, various profiler types have been developed. Yet, designing practical profilers often requires extensive expertise, adeptness optimization, significant implementation efforts. This results a void where aspirations for fast robust remain...