Spotting the Bottlenecks: A New Dataset for Optimizing Computer Vision Models

by

Author: Denis Avetisyan

Researchers have created a benchmark dataset and automated method to identify common performance issues within computer vision models, making optimization more accessible.

A benchmark dataset is constructed through a pipeline that distills application behaviors across diverse hardware, employing trace profiling and meticulously curated human feedback to establish a comprehensive evaluation resource for identifying the “Torch Trace” anti-pattern-a systematic weakness in performance optimization-and ensuring robust, provable algorithm efficiency, irrespective of underlying implementation details or platform variations, as quantified by $f(x) = \int_{a}^{b} g(x) dx$.
A benchmark dataset is constructed through a pipeline that distills application behaviors across diverse hardware, employing trace profiling and meticulously curated human feedback to establish a comprehensive evaluation resource for identifying the “Torch Trace” anti-pattern-a systematic weakness in performance optimization-and ensuring robust, provable algorithm efficiency, irrespective of underlying implementation details or platform variations, as quantified by $f(x) = \int_{a}^{b} g(x) dx$.

TorchTraceAP introduces a two-stage approach leveraging machine learning and large language models to detect and classify performance anti-patterns in PyTorch traces.

Optimizing computer vision models demands identifying subtle performance bottlenecks-a task typically requiring specialized infrastructure and deep expertise inaccessible to many researchers. To address this, we introduce TorchTraceAP: A New Benchmark Dataset for Detecting Performance Anti-Patterns in Computer Vision Models, comprising over 600 PyTorch traces and a novel two-stage detection method. This approach combines lightweight machine learning for initial anomaly localization with large language models for refined classification and targeted feedback, demonstrably outperforming traditional techniques. Will this automated approach democratize performance optimization and unlock new efficiencies in computer vision research and deployment?

The Inherent Limitations of Parallel Computation

The current landscape of deep learning is fundamentally shaped by the computational power of Graphics Processing Units (GPUs). These processors, originally designed for rendering images, have become indispensable for training and deploying complex neural networks due to their ability to perform massive parallel calculations. However, simply possessing GPU hardware doesn’t guarantee optimal performance. While GPUs offer the potential for significant acceleration, realizing that potential is often hampered by a variety of factors. These include the inherent complexities of parallel programming, the need for efficient data transfer between the CPU and GPU, and the limitations of memory bandwidth. Consequently, even with state-of-the-art hardware, researchers and engineers frequently encounter bottlenecks that prevent them from fully harnessing the available computational resources, driving ongoing efforts to optimize both algorithms and hardware utilization.

Despite the immense computational power of modern GPUs, realizing their full potential in deep learning applications is frequently hampered by performance bottlenecks stemming from inefficient code and suboptimal system configurations. These limitations aren’t necessarily inherent to the hardware itself, but rather arise from how software interacts with it – issues like excessive data transfer between the CPU and GPU, memory access patterns that don’t align with GPU architecture, or underutilization of parallel processing capabilities. Research demonstrates that targeted optimization – refining code for GPU-specific operations, adjusting batch sizes, and employing techniques like mixed-precision training – can yield dramatic improvements. In some instances, meticulously addressing these inefficiencies has resulted in up to an eightfold increase in both model training and inference speeds, highlighting the critical importance of software optimization alongside hardware investment.

This work proposes leveraging machine learning to automate and improve the performance of other machine learning models, bridging the gap between machine learning and systems engineering.
This work proposes leveraging machine learning to automate and improve the performance of other machine learning models, bridging the gap between machine learning and systems engineering.

Tracing Execution: Unveiling Hidden Computational Costs

The PyTorch ecosystem includes the `Torch Profiler`, a tool designed for collecting detailed execution traces of PyTorch models. These traces capture information about each operation within the model, including its start and end times, input and output tensors, and the device on which it was executed. Data collection occurs through various methods, such as manual instrumentation or automatic tracing via context managers. The resulting trace data is then structured into a timeline representation, enabling developers to visualize the flow of execution and identify potential performance bottlenecks. Trace data can be exported in multiple formats, including JSON and Chrome tracing format, facilitating analysis and integration with other performance profiling tools.

Execution traces generated by profiling tools like Torch Profiler record the order and runtime of each operation within a PyTorch model. This data allows developers to identify performance bottlenecks by highlighting operations with disproportionately long durations. Analysis focuses on both the total time spent within a specific operation and the frequency with which it is called; a frequently executed operation, even with a short duration, can contribute significantly to overall latency. By examining the sequence of operations, developers can also identify inefficiencies such as redundant computations or unnecessary data transfers, enabling targeted optimization efforts to improve model speed and resource utilization.

Torch Trace Anti-Patterns represent frequently occurring coding errors within PyTorch models that lead to demonstrable performance degradation. These patterns, revealed through analysis of execution traces collected by tools like Torch Profiler, include inefficient tensor manipulations, unnecessary data copies, and suboptimal operator choices. The TorchTraceAP methodology leverages detailed trace data to automatically identify these anti-patterns with improved accuracy compared to existing baseline detection techniques. This automated detection allows developers to proactively address performance issues and optimize their models without manual code review, resulting in significant speedups and reduced resource consumption.

The system detects torch trace anti-patterns through a training flow utilizing event and window encoders, culminating in a window anomaly score prediction model.
The system detects torch trace anti-patterns through a training flow utilizing event and window encoders, culminating in a window anomaly score prediction model.

Automated Anomaly Detection in Model Execution: A Principled Approach

TorchTraceAP is a new system designed for automated performance anomaly detection within PyTorch models. It operates by applying anomaly detection techniques to trace data generated by the Torch Profiler. This allows TorchTraceAP to identify performance anti-patterns-inefficient code execution patterns-without requiring manual intervention. Evaluations demonstrate that TorchTraceAP achieves greater accuracy in identifying these anti-patterns compared to existing baseline methods for performance analysis. The system’s core functionality centers on automatically flagging areas of code that deviate from expected performance characteristics as recorded in the profiler traces.

TorchTraceAP employs a two-stage encoding process to represent model execution traces as input for anomaly detection. Initially, Event Encoders convert individual trace events – such as operator calls and data transfers – into numerical vectors, capturing static information about each event. These event embeddings are then fed into a Transformer Encoder, a neural network architecture designed to model sequential data. The Transformer processes the event embeddings in the context of their temporal order, learning to represent the dynamic relationships and dependencies between events over time. This allows TorchTraceAP to capture the temporal dynamics of model execution, identifying patterns and deviations indicative of performance anti-patterns.

TorchTraceAP identifies inefficient code segments within model execution traces by leveraging encoded temporal dynamics. The system achieves high reasoning accuracy – exceeding baseline methods – when provided with contextual information regarding potential anti-patterns within a defined trace window. This allows developers to pinpoint performance bottlenecks, such as excessive kernel launch times or redundant memory accesses, and receive actionable insights for targeted optimization. The system’s ability to reason about trace data, when prompted with specific anti-pattern knowledge, significantly enhances its diagnostic capabilities and facilitates efficient performance tuning.

This work introduces a novel approach to detecting anomalies in PyTorch models by analyzing trace event attributes and calling stacks, offering a comparative advantage over existing video and time-series anomaly detection methods.
This work introduces a novel approach to detecting anomalies in PyTorch models by analyzing trace event attributes and calling stacks, offering a comparative advantage over existing video and time-series anomaly detection methods.

Scaling and Optimization: A Synergistic Approach to Accelerated Learning

Modern deep learning often demands computational resources exceeding those available on a single GPU. To address this, libraries such as Hugging Face Accelerate and DeepSpeed significantly extend the functionality of frameworks like $PyTorch$, enabling the distribution of training workloads across multiple GPUs and even multiple nodes. These tools abstract away much of the complexity associated with parallel processing, allowing researchers and engineers to scale their models with relative ease. By intelligently partitioning data and model parameters, these libraries facilitate training on datasets and with model architectures that would be impractical, if not impossible, to handle on a single device. This distributed approach not only accelerates training but also enables the development of larger, more complex models capable of achieving state-of-the-art performance on challenging tasks.

Automated performance profiling during distributed deep learning training is now achievable through the integration of TorchTraceAP with accelerated libraries such as Hugging Face Accelerate and DeepSpeed. This synergy allows developers to move beyond manual bottleneck identification, as TorchTraceAP dynamically monitors and analyzes the execution of models across multiple GPUs. The system captures detailed timing information and resource utilization data, pinpointing performance limitations with precision. This automated analysis drastically reduces the time required for optimization, enabling faster iteration and improved model efficiency. By continuously profiling during training, the system identifies and addresses issues related to data transfer, computational load, and memory access, ultimately accelerating the path to high-performance deep learning deployments.

A streamlined pathway to high-performance deep learning is now achievable through the integration of accelerated libraries with automated performance profiling. By combining tools like Hugging Face Accelerate and DeepSpeed – which distribute computational loads across multiple GPUs – with systems like TorchTraceAP, developers gain a comprehensive solution for model building and deployment. This synergy not only maximizes hardware resource utilization, drastically reducing training timelines, but also unlocks substantial performance gains. Studies indicate that this automated optimization process has the potential to improve model performance by up to eight times, representing a significant leap forward in efficiency and capability for complex machine learning tasks. The result is a faster, more effective route to deploying cutting-edge deep learning models.

TorchTraceAP applications exhibit a varied distribution of CUDA kernel usage.
TorchTraceAP applications exhibit a varied distribution of CUDA kernel usage.

The pursuit of optimization, as detailed in this work introducing TorchTraceAP, necessitates a rigorous, almost mathematical, approach to identifying inefficiencies. The dataset and two-stage detection method represent a formalized system for pinpointing performance anti-patterns-a search for demonstrable correctness in the face of complex model behavior. This aligns perfectly with Fei-Fei Li’s observation: “AI is not about automating intelligence; it’s about augmenting it.” The TorchTraceAP dataset doesn’t replace the need for human understanding of computer vision models; rather, it provides a disciplined framework-a provable starting point-for augmenting that understanding and achieving demonstrably improved performance, shifting the focus from empirical ‘works’ to verifiable results. The methodology presented prioritizes identifying and correcting demonstrable flaws, reinforcing the principle that, in the chaos of data, only mathematical discipline endures.

What Lies Ahead?

The introduction of TorchTraceAP, while a pragmatic step toward automating the detection of performance failings, merely highlights the deeper, almost philosophical, problem plaguing applied machine learning. The dataset itself is a snapshot; a catalog of existing inefficiencies. The true challenge isn’t identifying what is slow, but understanding why slowness consistently manifests. One suspects the underlying causes are rarely algorithmic novelty, but rather a systematic failure to rigorously apply mathematical principles to model construction and deployment.

Future work must move beyond pattern recognition. The current approach, reliant on lightweight models and large language models, feels… expedient. It trades mathematical certainty for empirical observation. A more elegant solution would involve formally verifying properties of traced execution graphs, proving the absence of known anti-patterns rather than probabilistically detecting them. The field needs tools that allow for the specification of performance invariants, and automated proof systems to ensure adherence.

Ultimately, the pursuit of optimization should not resemble an archaeological dig through flawed implementations. It requires a fundamental shift: a commitment to building models that are, by mathematical definition, efficient – not simply those that appear to be after extensive profiling. The beauty of an algorithm lies not in tricks, but in the consistency of its boundaries and predictability. Until this principle is embraced, the cycle of detecting and patching performance issues will inevitably continue.

Original article: https://arxiv.org/pdf/2512.14141.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

See also:

2025-12-17 11:29