# Performance Published articles for Performance. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Chrome Adds Experimental Metrics to Measure Website Ad Load DevFeed: [Chrome Adds Experimental Metrics to Measure Website Ad Load]() Original publisher: [Read original article]() Author: Alex Harper Published: 2026-09-17T15:00:00Z Content type: article Language: en Sources: [Web Designer Depot]() Topics: [Chrome](), [Web](), [data](), [User Experience](), [Google](), [developer tooling](), [Core Web Vitals]() Tags: [ad-density](), [ad-performance](), [ad-tech](), [advertising](), [chrome](), [chrome-user-experience-report](), [core-web-vitals](), [crux](), [devtools](), [google](), [google-chrome](), [online-advertising](), [page-experience](), [performance](), [publishers](), [user-experience](), [ux](), [ux-usability](), [web-design](), [web-development](), [web-performance](), [website-ads](), [website-optimization](), [website-performance]() ### AI overview Chrome has introduced four experimental metrics to the Chrome User Experience Report (CrUX) that quantify website ad load: ad count, ad density, network weight, and CPU weight. The aggregated data is public and can be accessed through CrUX Vis, APIs, and Chrome DevTools, although availability and evaluation thresholds remain limited. ### Source excerpt Chrome can now measure exactly how obnoxious a website's ads are, from how much of your screen they swallow to how much data and CPU time they burn. Those ad-stuffed websites can finally be measured, and the results are public. ## GNOME 51 released DevFeed: [GNOME 51 released]() Original publisher: [Read original article]() Author: corbet Published: 2026-09-17T13:08:12Z Content type: release Language: en Sources: [LWN.net]() Topics: [version](), [App](), [data](), [interface](), [file]() Tags: [application](), [changes](), [data](), [desktop](), [file](), [gnome](), [gnome-51](), [improvements](), [interface](), [maps](), [offline](), [performance](), [transit](), [version]() ### AI overview GNOME 51 has been released with performance improvements, offline data and improved transit information in Maps, a new file previewer interface, and other changes. ### Source excerpt Version 51 of the GNOME desktop environment has been released. The list of changes includes a number of performance improvements, offline data and better transit information in the Maps application, a new interface for the file previewer, and more. ## Steam Frame Review: Capable VR Headset for Power Users Who Will Tinker DevFeed: [Steam Frame Review: Capable VR Headset for Power Users Who Will Tinker]() Original publisher: [Read original article]() Author: Scott Hayden Published: 2026-09-16T22:52:10Z Content type: article Language: en Sources: [Road to VR]() Topics: [pc](), [Streaming](), [Users]() Tags: [feature](), [pc-vr-news-reviews](), [performance](), [review]() ### AI overview A review of Valve's Steam Frame VR headset finds that it combines strong standalone capabilities with easy Steam game streaming. Its flexibility and performance appeal to power users, but demanding standalone games may be difficult to run and the headset is less user-friendly than Quest 3. ### Source excerpt Steam Frame is finally here. It's Valve's second-ever VR headset, and a radically different product than its first. Did Valve pull off the vision? And, if so, who is Steam Frame for? Read on in our full review to find out. Table of Contents Steam Frame Review Summary Steam Frame is a unique headset that [...] The post Steam Frame Review - Power-user's Playground appeared first on Road to VR. ## Perplexity's AI agents helped build a database. They weren't allowed to run it. DevFeed: [Perplexity's AI agents helped build a database. They weren't allowed to run it.]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-16T21:51:15Z Content type: article Language: en Sources: [The New Stack]() Topics: [Database](), [DynamoDB](), [Rust](), [Artificial Intelligence](), [rocksdb](), [Amazon S3]() Tags: [agents](), [ai](), [ai-agents](), [ai-infrastructure](), [api](), [coding](), [coding-agents](), [database](), [databases](), [dynamodb](), [open-source](), [performance](), [perplexity](), [rocksdb](), [rust](), [s3]() ### AI overview Perplexity built CobbleDB, a Rust key-value store, after finding DynamoDB too costly and insufficiently controllable for its search workload. Coding agents helped develop it, but were not allowed to run it in production. Perplexity measured lower read latency and expects lower costs, with plans to open-source the database. ### Source excerpt Perplexity decided it was paying too much for DynamoDB and wasn't getting the control it wanted over read performance. So The post Perplexity's AI agents helped build a database. They weren't allowed to run it. appeared first on The New Stack. ## TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor DevFeed: [TensorRT Edge-LLM Completes the MLPerf Edge Agentic Benchmark 6.4x Faster on Jetson AGX Thor]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-16T20:37:07Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Jetson AGX Thor Developer Kit](), [Inference Performance](), [benchmarking](), [TensorRT](), [Large Language Model](), [quantization](), [Artificial Intelligence]() Tags: [agentic-ai-generative-ai](), [ai-agent](), [ai-inference](), [evaluation](), [jetson](), [jetson-agx-thor-developer-kit](), [llm](), [llm-benchmarking](), [mlperf](), [nvidia](), [performance](), [quantization](), [tensorrt](), [tensorrt-llm](), [thor]() ### AI overview This article reports that NVIDIA TensorRT Edge-LLM ran Qwen3.6-27B on a single NVIDIA Jetson AGX Thor Developer Kit for the MLPerf Inference v6.1 Edge Agentic benchmark. Using NVFP4 quantization, tree-based multi-token prediction, and KV cache reuse, it achieved 52.33 tokens per second and completed 1,007 turns in 24 minutes and 36 seconds, 6.4 times faster than the llama.cpp reference submission. ### Source excerpt AI agents are moving from cloud data centers to vehicles, robots, and other edge devices. Unlike a chatbot that answers a single prompt, an agent works through... ## First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results DevFeed: [First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results]() Original publisher: [Read original article]() Author: vmwareblogs Published: 2026-09-16T18:20:25Z Content type: release Language: en Sources: [VMware Blogs]() Topics: [vcf 9.1](), [benchmarking](), [Benchmark](), [virtualization](), [Hardware]() Tags: [benchmarking](), [benchmarks](), [cloud-infrastructure](), [dell](), [efficiency](), [home-page](), [performance](), [vcf-9-1](), [vmmark](), [vmware](), [vmware-cloud-foundation](), [vsphere](), [vsphere-9-1]() ### AI overview VMware reports Dell Technologies benchmark results using VMware Cloud Foundation 9.1 and VMmark 4.1. The article describes VMmark 4.1's power-performance measurement and reports higher performance and tile count for VCF 9.1 than VCF 5.2 in the tested environment. ### Source excerpt We're excited to announce two new VMmark results today from Dell Technologies: First VCF 9.1 Benchmarks: These are the first results using VMware Cloud Foundation (VCF) 9.1. VCF 9.1 maximizes hardware efficiency using a Next-Gen Topology-Aware CPU Scheduler that optimizes memory and cache locality for intensive enterprise workloads. A separate VCF 9.1 evaluation demonstrated a ... Continued The post First VMmark 4.1 Power-Performance and VMware Cloud Foundation 9.1 Results appeared first on VMware Blogs. ## NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut DevFeed: [NVIDIA Vera Rubin NVL72 Delivers Leading Performance in MLPerf Inference v6.1 Debut]() Original publisher: [Read original article]() Author: Zhihan Jiang Published: 2026-09-16T15:00:48Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [benchmarking](), [Inference Performance](), [Dynamo](), [vllm](), [Nvidia](), [TensorRT-LLM]() Tags: [ai-infrastructure](), [benchmarks](), [dynamo](), [efficiency](), [hardware](), [inference](), [mlperf](), [networking](), [nvidia](), [nvidia-vera-rubin](), [nvl72](), [optimization](), [performance](), [software](), [tensorrt](), [tensorrt-llm](), [vera-rubin-nvl72](), [vllm]() ### AI overview NVIDIA reports MLPerf Inference v6.1 preview results for Vera Rubin NVL72 and GB300 NVL72 systems. Vera Rubin NVL72 delivered up to 3.7x higher throughput than GB300 NVL72 on Qwen3-VL and up to 2.5x higher throughput on DeepSeek-R1, while a four-rack GB300 NVL72 submission achieved 99% scaling efficiency. The results used vLLM, NVIDIA Dynamo, and TensorRT-LLM. ### Source excerpt System performance, efficient infrastructure scaling and continuous software optimization are key levers that determine AI inference economics. Higher system performance means more tokens generated, resulting in higher revenue. Efficient scaling means throughput grows proportionally as hardware gets added, requiring fewer resources to serve users at scale. Continuous optimization means generating more value from infrastructure investments. [...] ## OpenVDB Introduces SIMD Framework With Some 2~4x Performance Improvements DevFeed: [OpenVDB Introduces SIMD Framework With Some 2~4x Performance Improvements]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-16T10:05:23Z Content type: news Language: en Sources: [Phoronix]() Topics: [Data structures](), [Tooling](), [x86](), [releases](), [cudnn](), [Open Source]() Tags: [avx](), [command-line](), [cuda](), [data](), [data-structure](), [desktop-linux](), [github](), [kernels](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [ubuntu-benchmarks](), [ubuntu-hardware](), [x86]() ### AI overview OpenVDB 13.1 introduces a SIMD framework using Agner Fog's VectorClass Library for explicit x86 vectorization up to AVX-512. Adapted point transfer algorithms reportedly achieve 2x to 4x performance improvements on modern AVX-512 x86_64 CPUs. The release also includes NanoVDB CUDA resource-management and kernel improvements, plus updates to vdb_tool. ### Source excerpt OpenVDB is the sparse volume data structure and tooling library maintained by the Academy Software Foundation. OpenVDB in turn is used by various CGI software for dealing with sparse volumetric data such as Houdini, RenderMan, and Cinema 4D to the open-source Blender. It's even won an Academy Award for technical achievement while now in 2026 it's finally establishing a SIMD framework for better leveraging modern x86 ISA capabilities... ## Go + Services = One Goliath Project DevFeed: [Go + Services = One Goliath Project]() Original publisher: [Read original article]() Author: Khan Academy Published: 2019-12-20T23:00:00Z Content type: opinion Language: en Sources: [Khan Academy]() Topics: [Go Language](), [Python](), [Back end](), [Kotlin](), [Server]() Tags: [backend](), [end-of-life](), [engineering](), [go](), [infrastructure](), [kotlin](), [news](), [performance](), [services]() ### AI overview Khan Academy describes its effort to rebuild server software on Go's modern stack. The article explains why moving from Python 2 to Python 3 offered limited benefits, considers Kotlin and other options, and presents faster languages as a way to improve responsiveness and reduce server costs. ### Source excerpt By Kevin Dangoor Go + Services = One Goliath Project Khan Academy is embarking on a huge effort ... Read more ## Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving DevFeed: [Pinterest's Manas Search Platform Uses Quantization and SSD-Based Serving]() Original publisher: [Read original article]() Author: Olimpiu Pop Published: 2026-09-16T06:06:00Z Content type: news Language: en Sources: [InfoQ]() Topics: [quantization](), [AI search](), [webgpu]() Tags: [algorithms](), [architecture-design](), [development](), [infrastructure](), [news](), [optimization](), [performance](), [pinterest-search](), [quantization](), [search](), [ssd](), [vector]() ### AI overview Pinterest Engineering enhanced its Manas distributed search platform with scalar and product quantization, SSD-based serving, and late-interaction retrieval. The reported evaluations describe trade-offs among index size, recall, throughput, latency, and serving cost. ### Source excerpt Pinterest Engineering has enhanced its Manas search platform to manage vast data, improving efficiency in search and discovery functions. By applying Scalar and Product Quantization, memory usage decreased significantly while maintaining high recall rates. The platform utilizes SSDs for optimized performance, and it is transitioning to multi-vector models for refined relevance matching. By Olimpiu Pop ## Size-Specialized Memory Allocation DevFeed: [Size-Specialized Memory Allocation]() Original publisher: [Read original article]() Author: Michael Matloob Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [The Go Blog]() Topics: [Go](), [optimize]() Tags: [collector](), [compiler](), [go](), [performance](), [spans]() ### AI overview Go 1.27 introduces size-specialized allocation functions for allocations smaller than 80 bytes. The change makes those allocations up to 20-30% faster and can improve allocation-heavy programs by up to 1%. ### Source excerpt Go 1.27 improves performance of small allocations using size-specialized allocation functions. ## Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation DevFeed: [Trajectory as the Teacher: Few-Step Discrete Flow Matching via Energy-Navigated Distillation]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [text-generation](), [Algorithms](), [Language models](), [Inference]() Tags: [inference](), [language-models](), [performance](), [perplexity](), [research](), [text-generation](), [tokens]() ### AI overview The article introduces Trajectory-Shaped Discrete Flow Matching, a training method that guides intermediate trajectory decisions with an energy-based coherence measure. The authors argue that poor distillation trajectories, rather than insufficient student capacity, limit few-step generation. On a 170M-parameter language-modeling task, an 8-step student reportedly achieves lower perplexity than a 1,024-step teacher while reducing inference steps. ### Source excerpt Discrete flow matching generates text by iteratively transforming noise tokens into coherent language, but may require hundreds of forward passes. Distillation uses the multi-step trajectory to train a student to reproduce the process in a few steps. When the student underperforms, the usual explanation is insufficient capacity. We argue the opposite: the trajectory is the bottleneck, not the student. Each training trajectory is built through a chain of blind stochastic jumps with no evaluation of sequence quality; a single bad decision at an early midpoint propagates through subsequent steps... ## ML based ranking using Nrtsearch DevFeed: [ML based ranking using Nrtsearch]() Original publisher: [Read original article]() Author: Mohammad Mohtasham (Software Engineer); Tao Yu (Software Engineer) Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Inference](), [Scalability](), [Latency](), [recommendation systems](), [Open Source]() Tags: [bridge](), [inference](), [machine-learning](), [overhead](), [performance](), [pipeline](), [scalability](), [service]() ### AI overview Yelp's Nrtsearch Inference Plugin embeds machine-learning ranking directly in the search layer. The article explains the scoring workflow, including model configuration, feature extraction, candidate ranking, and application-specific business logic. It describes how co-locating feature storage and inference reduces network transfer, serialization overhead, and latency compared with a standalone inference service. ### Source excerpt We've extended Nrtsearch with the Inference Plugin, which embeds ML-based ranking directly in the search layer -- eliminating the need for a standalone scoring service. We use Nrtsearch (read more information on the blog post), a Lucene-based open-source search engine built by Yelp, to power a variety of applications such as business search, reviews search, ad delivery and photo search. In this blog post, we give a high-level overview of the Machine Learning (ML) based scoring workflow in Nrtsearch. We'll show how ML models are configured and loaded, and how different applications use custom business logic to develop, test, and... ## Introducing TIN: full-text search for Postgres DevFeed: [Introducing TIN: full-text search for Postgres]() Original publisher: [Read original article]() Author: Patrick Reynolds Published: 2026-09-16T00:00:00Z Content type: release Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [benchmarking]() Tags: [backups](), [benchmark](), [benchmarks](), [bm25](), [engineering](), [full-text-search](), [index](), [performance](), [postgres](), [reddit](), [replication](), [search](), [text](), [wikipedia]() ### AI overview PlanetScale announces TIN, a full-text search extension for Postgres and Neki databases. The article describes supported query and matching features, transaction and update behavior, and benchmark workloads and corpora used to assess performance. ### Source excerpt TIN is a fast, full-featured, full-text search index for Postgres ## Cornelis and Delos Data propose open alternatives to Nvidia's NVLink for AI scale-up networking DevFeed: [Cornelis and Delos Data propose open alternatives to Nvidia's NVLink for AI scale-up networking]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-15T20:56:48Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [NVLink](), [AI Networking](), [Nvidia](), [networking](), [Hardware](), [High-Performance Computing]() Tags: [ai](), [ai-networking](), [cornelis-networks](), [delos-data](), [hardware](), [high-performance-computing](), [network](), [networking](), [nvidia](), [nvlink](), [performance](), [software](), [systems]() ### AI overview The article reports that Cornelis Networks and Delos Data have entered the AI scale-up networking market with proposed alternatives to Nvidia's NVLink. Cornelis introduced its open Active Compute Fabric architecture, which combines programmable in-network compute with scale-up and scale-out networking, while the broader market is developing alternatives using protocols such as Ultra Ethernet and UALink. ### Source excerpt Intel spin-off Cornelis and newcomer Delos Data pitch open alternatives for scaling AI beyond the rack ## Micron Shows off 512GB DDR5 RDIMM: 12TB per Dual-Socket Server at 9,200 MT/s, Volume Production in 2H 2027 DevFeed: [Micron Shows off 512GB DDR5 RDIMM: 12TB per Dual-Socket Server at 9,200 MT/s, Volume Production in 2H 2027]() Original publisher: [Read original article]() Author: Brian Beeler Published: 2026-09-15T20:18:17Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [ddr5](), [servers](), [intel](), [cpu]() Tags: [capacity](), [ddr5](), [enterprise](), [generation](), [intel](), [memory](), [modules](), [performance](), [production](), [release](), [server](), [speed](), [volume]() ### AI overview Micron demonstrated a 512GB DDR5 RDIMM rated for up to 9,200 MT/s. The module can provide 12TB of memory in a 24-slot dual-socket server, with volume production scheduled for the second half of 2027. AMD and Intel are validating it for next-generation server platforms. ### Source excerpt Micron has demonstrated a 512GB DDR5 RDIMM running on multiple server platforms, which it calls the world's first module at that capacity, and says AMD and Intel are both validating it for their next-generation server platforms. The module is rated for speeds up to 9,200 MT/s, and in a 24-slot dual-socket server it puts 12TB The post Micron Shows off 512GB DDR5 RDIMM: 12TB per Dual-Socket Server at 9,200 MT/s, Volume Production in 2H 2027 appeared first on StorageReview.com. ## Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each DevFeed: [Dense vs. MoE Models: Active Parameters, Throughput, and When to Choose Each]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-09-15T17:00:11Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Mixture of Experts (MoE)](), [Nemotron](), [Routing (disambiguation)]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [architecture](), [cost](), [data-center-cloud](), [deployment](), [llms](), [memory](), [mixture-of-experts-moe](), [models](), [moe](), [nemo](), [nemotron](), [performance](), [router](), [routing](), [tokens]() ### AI overview This article explains how dense and Mixture-of-Experts models activate parameters, compares their effects on throughput, memory cost, and serving complexity, and discusses when each architecture fits different deployment constraints. It uses Nemotron 3.5 Lightning as an example of an MoE model. ### Source excerpt How can a 30B-parameter model activate only 3B parameters per token, and still use the capacity of the larger model? Nemotron 3.5 Lightning illustrates the... ## Optimizing CPU-side Rendering Code DevFeed: [Optimizing CPU-side Rendering Code]() Original publisher: [Read original article]() Author: Clay John Published: 2026-09-15T17:00:00Z Content type: tutorial Language: en Sources: [Godot Engine Official]() Topics: [Godot](), [Optimization](), [cpu](), [GPU](), [shaders]() Tags: [batching](), [bug](), [cpu](), [gpu](), [optimization](), [performance](), [performance-optimization](), [progress-report](), [shaders]() ### AI overview This article explains how Godot optimizes CPU-side rendering code. It describes balancing CPU and GPU workloads, identifying performance bottlenecks, investigating solutions, measuring results, and repeating the process. ### Source excerpt Optimizing CPU code is a lot of fun. Here's how we do it ## How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin DevFeed: [How NVIDIA Groq 3 LPX Deterministic Execution Drives Power-Efficient High-Interactivity Inference on NVIDIA Vera Rubin]() Original publisher: [Read original article]() Author: Tanya Lenz Published: 2026-09-15T16:55:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Groq 3 LPX](), [LPX](), [NVIDIA Vera Rubin](), [Vera Rubin NVL72](), [groq](), [Inference](), [Low Latency](), [long-context]() Tags: [agentic-ai-generative-ai](), [ai-factory](), [ai-inference](), [ai-platform](), [drive](), [dsx](), [groq](), [groq-3-lpx](), [inference](), [inference-performance](), [latency](), [low-latency](), [lpx](), [nvidia-vera-rubin](), [nvl72](), [performance](), [power-management](), [vera-rubin](), [vera-rubin-nvl72]() ### AI overview This NVIDIA developer article explains how Groq 3 LPX uses deterministic execution across 256 LPU chips to support low-latency inference on NVIDIA Vera Rubin. It describes compiler-scheduled execution and power-management techniques including Preemptive Power and Clock Period Synthesis. ### Source excerpt Power is a defining constraint for AI factories. As AI workloads demand a full compute platform to serve them, each component of that platform must maximize... ## Ubuntu 26.10 Set To Deliver Better Performance For Intel Core 3 Wildcat Lake DevFeed: [Ubuntu 26.10 Set To Deliver Better Performance For Intel Core 3 Wildcat Lake]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-15T15:34:07Z Content type: comparison Language: en Sources: [Phoronix]() Topics: [Ubuntu](), [Intel Core](), [intel](), [Hardware](), [Linux](), [SOC]() Tags: [comparison](), [core](), [desktop-linux](), [graphics](), [hardware](), [intel-core](), [kernel](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [nvme](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [release](), [soc](), [software](), [ubuntu](), [ubuntu-benchmarks](), [ubuntu-hardware](), [updates](), [upgrade](), [upgrades](), [wildcat-lake]() ### AI overview The article compares Ubuntu 26.04 LTS with Ubuntu 26.10 in development on a CHUWI UniBook equipped with an Intel Core 3 304 SoC, 8GB of RAM, and a 256GB NVMe SSD. It reports that Ubuntu 26.10's newer Linux kernel, Mesa graphics stack, and other software upgrades are expected to improve performance on Intel Wildcat Lake hardware. ### Source excerpt While Ubuntu 26.04 LTS is working fine out-of-the-box on new Intel Core 3 "Wildcat Lake" laptops like the CHUWI UniBook, next month's release of Ubuntu 26.10 will help deliver better performance out of these low-cost laptop options. ## \[$\] Adding BPF to blk-iocost DevFeed: [\[$\] Adding BPF to blk-iocost]() Original publisher: [Read original article]() Author: corbet Published: 2026-09-15T14:37:30Z Content type: article Language: en Sources: [LWN.net]() Topics: [Kernel](), [IO](), [ordering](), [Operating system]() Tags: [kernels](), [loading](), [ordering](), [performance]() ### AI overview This article discusses a patch series that would make the Linux blk-iocost I/O controller more flexible by allowing a BPF program to make cost decisions. It places the change in the context of block I/O scheduling for modern solid-state drives, where fairness and high throughput are important. ### Source excerpt The scheduling of block I/O requests has long been a challenge for operating-system kernels. For many years, the performance characteristics of rotating drives meant that putting considerable resources into request ordering was worthwhile. In a world with fast, solid-state drives, scheduling is more concerned with enforcing fairness between competing users while being fast enough to keep up with drives that can perform millions of I/O operations per second. The blk-iocost I/O controller was designed for the solid-state world and generally performs well, but there is always a desire to do better. This patch series from Tao Cui aims to make blk-iocost more flexible by enabling the loading of a BPF program to make cost decisions. ## Subnormal floating-point numbers are expensive... on Intel processors DevFeed: [Subnormal floating-point numbers are expensive... on Intel processors]() Original publisher: [Read original article]() Author: Daniel Lemire Published: 2026-09-15T12:54:32Z Content type: article Language: en Sources: [Daniel Lemire]() Topics: [floating-point](), [Benchmark](), [intel](), [C++](), [Cache]() Tags: [benchmark](), [c-plus-plus](), [floating-point](), [intel](), [linux](), [numbers](), [performance](), [processors]() ### AI overview This article benchmarks the performance cost of IEEE subnormal floating-point values across Intel, AMD, Arm, and Apple processors. It reports that Intel multiplications involving subnormals can be about 45 to 50 times slower than normal multiplications, while additions and subtractions remain at full speed. AMD Zen 5 performs much better in the tested workloads. ### Source excerpt We represent floating-point numbers using the IEEE standard. For very small numbers, the standard uses special subnormal numbers. Unfortunately, they have a reputation of making operations slow. Thus video game programmers and machine learning specialists sometimes avoid computing with subnormal numbers for performance. How slow are they? Let me measure. I wrote a small C++ ... Continue reading Subnormal floating-point numbers are expensive... on Intel processors ## Performance Improvements in .NET 11 DevFeed: [Performance Improvements in .NET 11]() Original publisher: [Read original article]() Author: Stephen Toub - MSFT Published: 2026-09-15T12:00:00Z Content type: article Language: en Sources: [.NET Blog]() Topics: [.NET](), [.NET 11]() Tags: [blog](), [improvements](), [net](), [net-11](), [performance](), [post]() ### AI overview This post surveys hundreds of performance improvements in .NET 11, including runtime and library changes that reduce bounds checks, allocations, locks, loop cycles, redundant checks, instructions, system calls, and array-copy costs. ### Source excerpt Take a tour through hundreds of performance improvements in .NET 11. The post Performance Improvements in .NET 11 appeared first on .NET Blog. ## Performance improvements in Percona Server 8.4.11-11 DevFeed: [Performance improvements in Percona Server 8.4.11-11]() Original publisher: [Read original article]() Author: Bogdan Degtyariov Published: 2026-09-15T11:52:54Z Content type: article Language: en Sources: [Blog - Percona]() Topics: [Percona Server for MySQL](), [Percona](), [MySQL](), [Cache](), [Scalability](), [Database]() Tags: [benchmark](), [benchmarks](), [cache](), [io](), [mysql](), [percona](), [percona-server-for-mysql](), [performance](), [scalability]() ### AI overview This article describes performance and scalability improvements in Percona Server for MySQL 8.4.11-11, focusing on changes to the InnoDB buffer pool and page flushing. It explains how narrowing mutex coverage and using finer-grained latching allows physical reads to proceed more in parallel, particularly for read-heavy, I/O-bound workloads. ### Source excerpt Focusing on Percona Server 8.4.11-11 My previous post (Performance Progression of Percona Server for MySQL 8.4) did a brief review of the performance changes in Percona Server for MySQL 8.4 released in 2026. I recommend reading it first to better understand the material in this post. Version 8.4.11-11 includes patches that deliver significant improvements in ... Continued The post Performance improvements in Percona Server 8.4.11-11 appeared first on Percona. [Next page]()