Meta developed Strobelight, an internal performance profiling service built on open-source technologies like eBPF and Spark. It provides continuous, low-overhead profiling of their C++ services, allowing engineers to identify performance bottlenecks and optimize CPU usage without deploying special builds or restarting services. Strobelight leverages randomized sampling and aggregation to minimize performance impact while offering flexible filtering and analysis capabilities. This helps Meta improve resource utilization, reduce costs, and ultimately deliver faster, more efficient services to users.
The blog post details a misguided attempt to optimize a 2D convolution operation. The author initially focuses on vectorization using SIMD instructions, expecting significant performance gains. However, after extensive effort, the improvements are minimal. The root cause is revealed to be memory bandwidth limitations: the optimized code, while processing data faster, is ultimately bottlenecked by the rate at which it can fetch data from memory. This highlights the importance of profiling and understanding performance bottlenecks before diving into optimization, as premature optimization targeting the wrong area can be wasted effort. The author learns a valuable lesson: focus on optimizing memory access patterns and reducing cache misses before attempting low-level optimizations like SIMD.
HN commenters largely agreed with the blog post's premise that premature optimization without profiling is counterproductive. Several pointed out the importance of understanding the problem and algorithm first, then optimizing based on measured bottlenecks. Some suggested tools like perf and VTune Amplifier for profiling. A few challenged the author's dismissal of SIMD intrinsics, arguing their usefulness in specific performance-critical scenarios, especially when compilers fail to generate optimal code. Others highlighted the trade-off between optimized code and readability/maintainability, emphasizing the importance of clear code unless absolute performance is paramount. A couple of commenters offered additional optimization techniques like loop unrolling and cache blocking.
A recent Linux kernel change inadvertently broke eBPF programs relying on PT_REGS_RC(regs). Intended to optimize register access for x86, this change accidentally cleared the return value register before eBPF programs using kprobe and kretprobe could access it. This resulted in eBPF tools like bpftrace and bcc showing garbage data instead of expected return values. The issue primarily affects x86 systems running kernel versions 6.5 and later and has already been fixed in 6.5.1, 6.4.12, and 6.1.38. Users of affected kernels should update to receive the fix.
The Hacker News comments discuss the complexities and nuances of the issue presented in the article about pt_regs returning garbage in recent Linux kernels due to changes introduced by "Fred." Several commenters express sympathy for Fred, highlighting the challenging trade-offs inherent in kernel development, especially when balancing performance optimizations with backward compatibility. Some point out the difficulties of maintaining eBPF programs across kernel versions and the lack of clear documentation or warnings about these breaking changes. Others delve into the technical specifics, discussing register context, stack unwinding, and the implications for debuggers and profiling tools. The overall sentiment seems to be one of acknowledging the difficulty of the situation and the need for better communication and tooling to navigate such kernel-level changes. A few users also suggest potential workarounds and debugging strategies.
Ninjavis is a tool that visualizes Ninja build logs, providing insights into build processes. It parses the log file to create an interactive HTML visualization displaying the dependencies between build targets and their execution times. This allows developers to quickly identify bottlenecks, parallelisms, and dependencies within their builds, facilitating optimization and debugging. The visualization includes features like zooming, panning, and searching, making it easier to navigate complex build graphs and understand the flow of the build process.
Hacker News users generally praised ninjavis for its potential usefulness in debugging and optimizing build processes. Several commenters pointed out the difficulty of parsing Ninja logs and appreciated a tool that could provide a visual representation. Some suggested desired features like the ability to filter by target or to integrate with existing build visualization tools like Chrome's tracing. One commenter expressed concern about the project's reliance on Python's regular expressions for parsing, suggesting it might be brittle. Another mentioned potential for improvement by leveraging Ninja's -t query functionality for more robust data extraction. Overall, the comments reflect a positive reception to the tool, with an emphasis on its practical applications for developers.
The blog post "Putting Andrew Ng's OCR models to the test" evaluates the performance of two optical character recognition (OCR) models presented in Andrew Ng's Deep Learning Specialization course. The author tests the models, a simpler CTC-based model and a more complex attention-based model, on a dataset of synthetically generated license plates. While both models achieve reasonable accuracy, the attention-based model demonstrates superior performance, particularly in handling variations in character spacing and length. The post highlights the practical challenges of deploying these models, including the need for careful data preprocessing and the computational demands of the attention mechanism. It concludes that while Ng's course provides valuable foundational knowledge, real-world OCR applications often require further optimization and adaptation.
Several Hacker News commenters questioned the methodology and conclusions of the original blog post. Some pointed out that the author's comparison wasn't fair, as they seemingly didn't fine-tune the models properly, particularly the transformer model, leading to skewed results in favor of the CNN-based approach. Others noted the lack of details on training data and hyperparameters, making it difficult to reproduce the results or draw meaningful conclusions about the models' performance. A few suggested alternative OCR tools and libraries that reportedly offer better accuracy and performance. Finally, some commenters discussed the trade-offs between CNNs and transformers for OCR tasks, acknowledging the potential of transformers but emphasizing the need for careful tuning and sufficient data.
The blog post argues for a more holistic approach to debugging and performance analysis by combining various tools and data sources. It emphasizes the limitations of isolated tools like memory profilers, call graphs, exception reports, and telemetry, advocating instead for integrating them to provide "system-wide context." This richer context allows developers to understand not only what went wrong, but also why and how, enabling more effective and efficient troubleshooting. The post uses a fictional scenario involving a slow web service to illustrate how correlating data from different tools can pinpoint the root cause of a performance issue, which in their example turns out to be an unexpected interaction between a third-party library and the application's caching strategy.
Hacker News users discussed the blog post about system-wide context, focusing primarily on the practical challenges of implementing such a system. Several commenters pointed out the difficulty of handling circular dependencies and the potential performance overhead, particularly in garbage-collected languages. Some suggested alternative approaches like structured logging and distributed tracing, while others questioned the overall value proposition compared to existing debugging tools. The complexity of integrating with different programming languages and the potential for information overload were also raised as concerns. A few commenters expressed interest in the idea but acknowledged the significant engineering effort required to make it a reality. One compelling comment highlighted the potential benefits for debugging complex, distributed systems, where understanding the interplay of different components is crucial.
Perforator is an open-source, cluster-wide profiling tool developed by Yandex for analyzing performance in large data centers. It uses hardware performance counters to collect low-overhead, detailed performance data across thousands of machines simultaneously, aiming to identify performance bottlenecks and optimize resource utilization. The tool offers a web interface for visualization and analysis, and allows users to drill down into specific nodes and processes for deeper investigation. Perforator supports various profiling modes, including CPU, memory, and I/O, and can be integrated with existing monitoring systems.
Several commenters on Hacker News expressed interest in Perforator, particularly its ability to profile at scale and its low overhead. Some questioned the choice of Python for the agent, citing potential performance issues, while others appreciated its ease of use and integration with existing Python-based infrastructure. A few commenters compared it favorably to existing tools like BCC and eBPF, highlighting Perforator's distributed nature as a key differentiator. The discussion also touched on the challenges of profiling in production environments, with some sharing their experiences and suggesting potential improvements to Perforator. Overall, the comments indicated a positive reception to the tool, with many eager to try it in their own environments.
Voyage's blog post details their evaluation of various code embedding models for code retrieval tasks. They emphasize the importance of using realistic datasets and evaluation metrics like Mean Reciprocal Rank (MRR) tailored for code search scenarios. Their experiments demonstrate that retrieval performance varies significantly across datasets and model architectures, with specialized models like CodeT5 consistently outperforming general-purpose embedding models. They also found that retrieval effectiveness plateaus as embedding dimensionality increases beyond a certain point, suggesting diminishing returns for larger embeddings. Finally, they introduce a novel evaluation dataset derived from Voyage's internal codebase, aimed at providing a more practical benchmark for code retrieval models in real-world settings.
Hacker News users discussed the methodology of Voyage's code retrieval evaluation, particularly questioning the reliance on HumanEval and MBPP benchmarks. Some argued these benchmarks don't adequately reflect real-world code retrieval scenarios, suggesting alternatives like retrieving code from a large corpus based on natural language queries. The lack of open-sourcing for Voyage's evaluated models and datasets also drew criticism, hindering reproducibility and broader community engagement. There was a brief discussion on the usefulness of keyword search as a strong baseline and the potential benefits of integrating semantic search techniques. Several commenters expressed interest in seeing evaluations based on more realistic use cases, including bug fixing or adding new features within existing codebases.
DeepSeek's R1-Zero and R1 models demonstrate impressive performance in language modeling, outperforming open-source models of comparable size in several benchmarks. R1-Zero, despite being pre-trained on only 1.5 trillion tokens, achieves similar performance to much larger open-source models trained on 3-4 trillion tokens. The more powerful R1 model, trained with selected data and reinforcement learning from human feedback, further improves upon R1-Zero, especially in reasoning and following instructions. DeepSeek attributes its success to a combination of improved architecture, efficient training, and high-quality data. The results highlight the potential for achieving high performance with smaller, more efficiently trained models.
HN commenters discuss the implications of DeepSeek's impressive results in the ARC (Abstraction and Reasoning Corpus) challenge with their R1-Zero and R1 models. Several highlight the significance of achieving near-perfect scores on the training set, raising questions about the nature of generalization and the potential limitations of current evaluation metrics. Some express skepticism about the actual novelty of the approach, noting similarities to existing techniques and questioning the impact of architectural choices versus data augmentation. The closed nature of DeepSeek and the lack of publicly available code also draw criticism, with some suspecting potential overfitting or undisclosed tricks. Others emphasize the importance of reproducible research and open collaboration for scientific progress in the field. The potential for such powerful models in practical applications is acknowledged, with some speculating on future developments and the need for better benchmarks.
Summary of Comments ( 7 )
https://news.ycombinator.com/item?id=43290555
Hacker News commenters generally praised Facebook/Meta's release of Strobelight as a positive contribution to the open-source profiling ecosystem. Some expressed excitement about its use of eBPF and its potential for performance analysis. Several users compared it favorably to other profiling tools, noting its ease of use and comprehensive data visualization. A few commenters raised questions about its scalability and overhead, particularly in large-scale production environments. Others discussed its potential applications beyond the initially stated use cases, including debugging and optimization in various programming languages and frameworks. A small number of commenters also touched upon Facebook's history with open source, expressing cautious optimism about the project's long-term support and development.
The Hacker News post discussing Facebook's Strobelight profiling service generated several comments, mostly focusing on comparisons with existing profiling tools and some skepticism about Facebook's open-source contributions.
One commenter highlights the similarities between Strobelight and existing open-source continuous profiling tools like Parca, pyroscope, and conprof, questioning the novelty of Facebook's solution. They suggest that Facebook could have contributed to these projects instead of creating a new one. This sentiment is echoed by another user who mentions contributing to async-profiler, a Java profiler, and expresses disappointment that large companies often reinvent the wheel instead of collaborating with existing open-source efforts.
Another commenter focuses on the perceived "open-washing" aspect, arguing that Facebook's history with open source has been more about taking than giving back. They express doubt that Strobelight will be truly open and actively maintained, suggesting it might be abandoned like other Facebook open-source projects.
A few users discuss the technical details of Strobelight, comparing its eBPF-based approach with other profiling methods and speculating about its performance characteristics. One commenter mentions using a custom-built eBPF profiler similar to Strobelight and shares their experience, providing a practical perspective on the technology.
Some comments also touch upon the challenges of profiling in production environments and the complexities of performance analysis. One user raises the question of whether Strobelight addresses the issue of "noisy neighbors" in shared infrastructure, highlighting a common problem in cloud-native environments.
Overall, the comments express a mix of curiosity about the technical aspects of Strobelight, skepticism about Facebook's open-source commitment, and comparisons with existing profiling solutions. Several users advocate for collaboration with existing open-source projects instead of reinventing the wheel. The conversation provides a glimpse into the perspectives of developers and engineers familiar with profiling tools and the challenges of performance optimization.