Llov

Bora, Utpal; Das, Santanu; Kukreja, Pankaj; Joshi, Saurabh; Upadrasta, Ramakrishna; Rajopadhye, Sanjay

doi:10.1145/3418597

Cited by 20 publications

(5 citation statements)

References 46 publications

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“…• LLOV [8] is a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework. • Intel Inspector [29] is a dynamic analysis tool that detects threading and memory errors in C, C++ and Fortran codes.…”

Section: Baselinesmentioning

confidence: 99%

“…While dynamic approaches, like Intel Inspector [29], are commonly used, there is growing interest in static data race detection tools that complement dynamic approaches. LLOV [8] is a recent tool evaluated with DRB. However, designing such tools demands significant data race expertise and running code snippets, making the process time-consuming.…”

Section: Introductionmentioning

confidence: 99%

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HPC-GPT: Integrating Large Language Model for High-Performance Computing

Ding,

Chen,

Emani

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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Large Language Models (LLMs), including the LLaMA model, have exhibited their efficacy across various general-domain natural language processing (NLP) tasks. However, their performance in highperformance computing (HPC) domain tasks has been less than optimal due to the specialized expertise required to interpret the model's responses. In response to this challenge, we propose HPC-GPT, a novel LLaMA-based model that has been supervised finetuning using generated QA (Question-Answer) instances for the HPC domain. To evaluate its effectiveness, we concentrate on two HPC tasks: managing AI models and datasets for HPC, and data race detection. By employing HPC-GPT, we demonstrate comparable performance with existing methods on both tasks, exemplifying its excellence in HPC-related scenarios. Our experiments on open-source benchmarks yield extensive results, underscoring HPC-GPT's potential to bridge the performance gap between LLMs and HPC-specific tasks. With HPC-GPT, we aim to pave the way for LLMs to excel in HPC domains, simplifying the utilization of language models in complex computing applications.

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Section: Baselinesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HPC-GPT: Integrating Large Language Model for High-Performance Computing

Ding,

Chen,

Emani

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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show abstract

“…A large amount of research work has been devoted to detecting data races in OpenMP programs [2,3,5,6,14,16,18]. Based on the detection mechanisms, there are typically two types of race detection tools: static and dynamic.…”

Section: Related Workmentioning

confidence: 99%

“…Static detection tools analyze program source code to discover data races, while dynamic tools detect data races by monitoring the execution of target programs. For example, LLOV [3] is a static data race detector based on LLVM, while ROMP [6] tracks accesses, access orderings, and mutual exclusion to detect data races. Different from these data race detection tools, OMPSanitizer mainly aims to reduce the manual effort required to investigate the data races reported by the tools.…”

Section: Related Workmentioning

confidence: 99%

“…That is, the two accesses may exhibit different execution results due to the lack of necessary synchronization to determine their happens-before order. To facilitate the detection of data races, a large amount of research effort has been devoted to automatic data race detection [2,3,5,6,14,18]. They typically employ a variety of static and dynamic program analysis techniques to figure out whether memory accesses issued by different threads may constitute data races.…”

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confidence: 99%

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Does it matter?

Wang¹,

Lin

2021

Proceedings of the ACM International Conference on Supercomputing

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Data races are a primary source of concurrency bugs in parallel programs. Yet, debugging data races is not easy, even with a large amount of data race detection tools. In particular, there still exists a manually-intensive and time-consuming investigation process after data races are reported by existing race detection tools. To address this issue, we present OMPSanitizer in this paper. OMPSanitizer employs a novel and semantic-aware impact analysis mechanism to assess the potential impact of detected data races so that developers can focus on data races with a high probability to produce a harmful impact. This way, OMPSanitizer can remove the heavy debugging burden of data races from developers and simultaneously enhance the debugging efficiency. We have implemented OMPSanitizer based on the widely-used dynamic binary instrumentation infrastructure, Intel Pin. Our evaluation results on a broad range of OpenMP programs from the DataRaceBench benchmark suite and an ECP Proxy application demonstrate that OMPSanitizer can precisely report the impact of data races detected by existing race detectors, e.g., Helgrind and ThreadSanitizer. We believe OMPSanitizer will provide a new perspective on automating the debugging support for data races in OpenMP programs. CCS CONCEPTS• Software and its engineering → Software testing and debugging; Parallel programming languages; Multithreading.

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A Study of Memory Anomalies in OpenMP Applications

Protze

Hernández

et al. 2020

Lecture Notes in Computer Science

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Incorrect usage of OpenMP constructs may cause different kinds of defects in OpenMP applications. Most of the existing work focuses on concurrency bugs such as data races and deadlocks, since concurrency bugs are difficult to detect and debug. In this paper, we discuss an under-examined defect in OpenMP applications: memory anomalies. These occur when the application issues illegal memory accesses that may result in a non-deterministic result or even a program crash. Based on the latest OpenMP 5.0 specification, we analyze some OpenMP usage errors that may lead to memory anomalies. Then we illustrate three kinds of memory anomalies: use of uninitialized memory (UUM), use of stale data (USD), and use after free (UAF). While all three anomalies can occur in sequential programs, their manifestations in parallel OpenMP programs can be different, and debugging such anomalies in the context of parallel programs also imposes an additional complexity relative to sequential programs. To measure the effectiveness of memory anomaly detectors on OpenMP applications, we have evaluated three state-of-theart tools with a group of micro-benchmarks. These micro-benchmarks are either selected from the DRACC benchmark suite or constructed from our own experience. The evaluation result shows that none of these tools can currently handle all three kinds of memory anomalies.

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Llov

Cited by 20 publications

References 46 publications

HPC-GPT: Integrating Large Language Model for High-Performance Computing

HPC-GPT: Integrating Large Language Model for High-Performance Computing

Does it matter?

A Study of Memory Anomalies in OpenMP Applications

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