A tiny code change in the Linux kernel could significantly reduce data center energy consumption. Researchers identified an inefficiency in how the kernel manages network requests, causing servers to wake up unnecessarily and waste power. By adjusting just 30 lines of code related to the network's power-saving mode, they achieved power savings of up to 30% in specific workloads, particularly those involving idle periods interspersed with short bursts of activity. This improvement translates to substantial potential energy savings across the vast landscape of data centers.
"Less Slow C++" offers practical advice for improving C++ build and execution speed. It covers techniques ranging from precompiled headers and unity builds (combining source files) to link-time optimization (LTO) and profile-guided optimization (PGO). It also explores build system optimizations like using Ninja and parallelizing builds, and coding practices that minimize recompilation such as avoiding unnecessary header inclusions and using forward declarations. Finally, the guide touches upon utilizing tools like compiler caches (ccache) and build analysis utilities to pinpoint bottlenecks and further accelerate the development process. The focus is on readily applicable methods that can significantly improve C++ project turnaround times.
Hacker News users discussed the practicality and potential benefits of the "less_slow.cpp" guidelines. Some questioned the emphasis on micro-optimizations, arguing that focusing on algorithmic efficiency and proper data structures is generally more impactful. Others pointed out that the advice seemed tailored for very specific scenarios, like competitive programming or high-frequency trading, where every ounce of performance matters. A few commenters appreciated the compilation of optimization techniques, finding them valuable for niche situations, while some expressed concern that blindly applying these suggestions could lead to less readable and maintainable code. Several users also debated the validity of certain recommendations, like avoiding virtual functions or minimizing branching, citing potential trade-offs with code design and flexibility.
The blog post explores using e-graphs, a data structure representing equivalent expressions, to create domain-specific languages (DSLs) within Python. By combining e-graphs with pattern matching and rewrite rules, users can define custom operations and optimizations tailored to their needs. The post introduces Egglog, a Python library built on this principle, demonstrating how it allows users to represent and manipulate mathematical expressions symbolically, perform automatic simplification, and even derive symbolic gradients. This approach bridges the gap between the flexibility of Python and the performance of specialized DSLs, enabling rapid prototyping and efficient execution of complex computations.
HN commenters generally expressed interest in Egglog and its potential. Several questioned its practicality for larger, real-world Python programs due to performance concerns and the potential difficulty of defining rules for complex codebases. Some highlighted the project's novelty and the cleverness of using e-graphs for optimization, drawing comparisons to other symbolic execution and program synthesis techniques. A few commenters also inquired about specific features, such as handling side effects and integration with existing Python tooling. There was also discussion around potential applications beyond optimization, including program analysis and verification. Overall, the sentiment was cautiously optimistic, acknowledging the early stage of the project but intrigued by its innovative approach.
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.
Summary of Comments ( 16 )
https://news.ycombinator.com/item?id=43749271
HN commenters are skeptical of the claimed 5-30% power savings from the Linux kernel change. Several point out that the benchmark used (SPECpower) is synthetic and doesn't reflect real-world workloads. Others argue that the power savings are likely much smaller in practice and question if the change is worth the potential performance trade-offs. Some suggest the actual savings are closer to 1%, particularly in I/O-bound workloads. There's also discussion about the complexities of power measurement and the difficulty of isolating the impact of a single kernel change. Finally, a few commenters express interest in seeing the patch applied to real-world data centers to validate the claims.
The Hacker News post, titled "Reworking 30 lines of Linux code could cut power use by up to 30 percent," linking to an IEEE Spectrum article about data center energy consumption, sparked a discussion with several insightful comments.
Many commenters focused on the specifics of the Linux kernel change mentioned in the title. Some expressed skepticism about the claimed 30% power savings, questioning the methodology used to arrive at that figure and pointing out that such a dramatic reduction likely applies only to very specific workloads or configurations. Others delved into the technical details of the code change, discussing the trade-offs involved and potential performance implications. There was a healthy dose of technical debate about how significant this change actually is and whether the headline accurately reflects the impact.
Several commenters broadened the discussion to the larger issue of data center energy consumption. They highlighted the importance of optimizing software for energy efficiency, not just relying on hardware improvements. Some pointed out that seemingly small code changes can have a significant cumulative impact when deployed across massive data centers. Others discussed the environmental impact of data centers and the need for greater sustainability efforts.
A few commenters mentioned related efforts to reduce energy consumption in other areas of computing, such as web browsers and mobile devices. This broadened the scope beyond just server-side Linux optimization.
Some questioned the practicality of applying these changes broadly, considering the potential for instability or unforeseen consequences in different system configurations. This brought a dose of realism to the discussion, reminding readers that potential gains need to be weighed against risks in complex systems.
Overall, the comments section reflects a mix of cautious optimism, technical scrutiny, and a broader awareness of the importance of energy efficiency in the computing world. Commenters engage with the specific code change mentioned in the headline while also connecting it to larger trends and concerns surrounding data center energy consumption. There's no outright dismissal of the proposed changes, but a healthy amount of critical analysis and questioning of the presented figures.