Interpreting Performance Profiles with Deep Learning

• URL: http://arxiv.org/abs/2508.02729v1
• Date: Fri, 01 Aug 2025 17:23:41 GMT
• Title: Interpreting Performance Profiles with Deep Learning
• Authors: Zhuoran Liu,
• Abstract summary: This thesis explores a new direction to combine performance profiles and program semantics with a deep learning approach.<n>We combine profiles generated by Async Profiler with code summarization from a fine-tuned CodeBERT-based model.<n>Our system can effectively assist analysis on many Java benchmarks.
• Score: 5.749821593008657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Profiling tools (also known as profilers) play an important role in understanding program performance at runtime, such as hotspots, bottlenecks, and inefficiencies. While profilers have been proven to be useful, they give extra burden to software engineers. Software engineers, as the users, are responsible to interpret the complex performance data and identify actionable optimization in program source code. However, it can be challenging for users to associate inefficiencies with the program semantics, especially if the users are not the authors of the code, which limits the applicability of profilers. In this thesis, we explore a new direction to combine performance profiles and program semantics with a deep learning approach. The key idea is to glean code summary for semantic information (at a certain level) and integrate it into a profiler, which can better understand program inefficiencies for actionable optimization. To be concrete, we combine profiles generated by Async Profiler (the state-of-the-art Java profiler) with code summarization from a fine-tuned CodeBERT-based model. We demonstrate the code summaries of any selected call path in a graphic user interface. Our system can effectively assist analysis on many Java benchmarks.

Related papers

• Do AI models help produce verified bug fixes? [62.985237003585674]
  Large Language Models are used to produce corrections to software bugs.<n>This paper investigates how programmers use Large Language Models to complement their own skills.<n>The results are a first step towards a proper role for AI and LLMs in providing guaranteed-correct fixes to program bugs.
  arXiv  Detail & Related papers  (2025-07-21T17:30:16Z)
• Training Large Recommendation Models via Graph-Language Token Alignment [53.3142545812349]
  We propose a novel framework to train Large Recommendation models via Graph-Language Token Alignment.<n>By aligning item and user nodes from the interaction graph with pretrained LLM tokens, GLTA effectively leverages the reasoning abilities of LLMs.<n> Furthermore, we introduce Graph-Language Logits Matching (GLLM) to optimize token alignment for end-to-end item prediction.
  arXiv  Detail & Related papers  (2025-02-26T02:19:10Z)
• DeepContext: A Context-aware, Cross-platform, and Cross-framework Tool for Performance Profiling and Analysis of Deep Learning Workloads [5.987963635879264]
  This paper introduces DeepContext, a novel profiler that links program contexts across high-level Python code, deep learning frameworks, underlying libraries written in C/C++, as well as device code executed on GPUs. DeepContext incorporates measurements of both coarse- and fine-grained performance metrics for major deep learning frameworks, such as PyTorch and JAX. In addition, DeepContext integrates a novel GUI that allows users to quickly identify hotpots and an innovative automated performance analyzer that suggests users with potential optimizations based on performance metrics and program context.
  arXiv  Detail & Related papers  (2024-11-05T04:15:26Z)
• Prompt Tuning as User Inherent Profile Inference Machine [53.78398656789463]
  We propose UserIP-Tuning, which uses prompt-tuning to infer user profiles. A profile quantization codebook bridges the modality gap by profile embeddings into collaborative IDs. Experiments on four public datasets show that UserIP-Tuning outperforms state-of-the-art recommendation algorithms.
  arXiv  Detail & Related papers  (2024-08-13T02:25:46Z)
• CompilerDream: Learning a Compiler World Model for General Code Optimization [58.87557583347996]
  We introduce CompilerDream, a model-based reinforcement learning approach to general code optimization.<n>It comprises a compiler world model that accurately simulates the intrinsic properties of optimization passes and an agent trained on this model to produce effective optimization strategies.<n>It excels across diverse datasets, surpassing LLVM's built-in optimizations and other state-of-the-art methods in both settings of value prediction and end-to-end code optimization.
  arXiv  Detail & Related papers  (2024-04-24T09:20:33Z)
• TAROT: A Hierarchical Framework with Multitask Co-Pretraining on Semi-Structured Data towards Effective Person-Job Fit [60.31175803899285]
  We propose TAROT, a hierarchical multitask co-pretraining framework, to better utilize structural and semantic information for informative text embeddings. TAROT targets semi-structured text in profiles and jobs, and it is co-pretained with multi-grained pretraining tasks to constrain the acquired semantic information at each level.
  arXiv  Detail & Related papers  (2024-01-15T07:57:58Z)
• Collaborative, Code-Proximal Dynamic Software Visualization within Code Editors [55.57032418885258]
  This paper introduces the design and proof-of-concept implementation for a software visualization approach that can be embedded into code editors. Our contribution differs from related work in that we use dynamic analysis of a software system's runtime behavior. Our visualization approach enhances common remote pair programming tools and is collaboratively usable by employing shared code cities.
  arXiv  Detail & Related papers  (2023-08-30T06:35:40Z)
• Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion [15.068025336990287]
  We show that existing code completion models do not yield good results on our completion task. We query a program analyzer for information relevant to a given function call, and consider ways to provide the analyzer results to different code completion models during inference and training. Our experiments show that providing access to the function implementation and function usages greatly improves the argument completion performance.
  arXiv  Detail & Related papers  (2023-06-01T06:25:58Z)
• Learning from Self-Sampled Correct and Partially-Correct Programs [96.66452896657991]
  We propose to let the model perform sampling during training and learn from both self-sampled fully-correct programs and partially-correct programs. We show that our use of self-sampled correct and partially-correct programs can benefit learning and help guide the sampling process. Our proposed method improves the pass@k performance by 3.1% to 12.3% compared to learning from a single reference program with MLE.
  arXiv  Detail & Related papers  (2022-05-28T03:31:07Z)
• Profile Guided Optimization without Profiles: A Machine Learning Approach [0.0]
  Profile guided optimization is an effective technique for improving the optimization ability of compilers based on dynamic behavior. We present a novel statistical approach to inferring branch probabilities that improves the performance of programs that are compiled without profile guided optimizations.
  arXiv  Detail & Related papers  (2021-12-24T22:49:21Z)
• IOHanalyzer: Detailed Performance Analyses for Iterative Optimization Heuristics [3.967483941966979]
  IOHanalyzer is a new user-friendly tool for the analysis, comparison, and visualization of performance data of IOHs. IOHanalyzer provides detailed statistics about fixed-target running times and about fixed-budget performance of the benchmarked algorithms. IOHanalyzer can directly process performance data from the main benchmarking platforms.
  arXiv  Detail & Related papers  (2020-07-08T08:20:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.