# IO Input/output (I/O) is the use of streams to read data from sources and write data to destinations in computer programs. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## \[$\] 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. ## New Linux Patches Improving Hibernation Speed In 2026 DevFeed: [New Linux Patches Improving Hibernation Speed In 2026]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-15T10:30:22Z Content type: news Language: en Sources: [Phoronix]() Topics: [Linux](), [Kernel](), [IO](), [qemu]() Tags: [2026](), [desktop-linux](), [kernel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-kernel](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [performance](), [phoronix](), [phoronix-test-suite](), [qemu](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview A Linux kernel patch series proposes faster hibernation by improving swap-slot allocation and batching image I/O. Preliminary QEMU testing reported 18-25% lower write times and 11-20% faster reads, but the patches are not complete. ### Source excerpt A new patch series being worked on is enhancing the Linux kernel's hibernation allocator to allow for faster performance when hibernating the system... ## Infineon RISC-V for Automotive at Hot Chips 2026 DevFeed: [Infineon RISC-V for Automotive at Hot Chips 2026]() Original publisher: [Read original article]() Author: Vic A Published: 2026-09-13T21:58:44Z Content type: article Language: en Sources: [ServeTheHome]() Topics: [RISC-V](), [Microcontroller](), [real-time](), [Low Latency](), [Architecture & Design](), [Security](), [IO](), [Inference Performance]() Tags: [2026](), [architecture](), [automotive](), [compute](), [cost](), [efficiency](), [infineon](), [latency](), [low-latency](), [microcontrollers](), [mobile](), [performance](), [posix](), [processors](), [real-time](), [risc-v](), [security]() ### AI overview Infineon presents RISC-V automotive processors for next-generation vehicle architectures. The article covers real-time control, low latency, power efficiency, security, zone controllers, central car computers, and heterogeneous workloads including DSP, AI inference, audio, and POSIX-based services. ### Source excerpt At Hot Chips 2026, Infineon presented a case for using RISC-V in various automotive processors in next-generation cars The post Infineon RISC-V for Automotive at Hot Chips 2026 appeared first on ServeTheHome. ## Linux server performance: Is disk I/O slowing your application? DevFeed: [Linux server performance: Is disk I/O slowing your application?]() Original publisher: [Read original article]() Author: Hayden James Published: 2026-09-12T16:11:54Z Content type: tutorial Language: en Sources: [Hayden James]() Topics: [Linux](), [IO](), [Server](), [Command-line interface](), [Processes](), [NVMe]() Tags: [blog](), [command-line](), [cpu](), [linux](), [load](), [mysql](), [nvme](), [performance](), [processes](), [request](), [server](), [ssd](), [storage](), [sysadmins](), [terminal]() ### AI overview This tutorial explains how disk I/O bottlenecks can cause high Linux load averages even when CPU usage is low. It shows how to use top, atop, and iotop on a LEMP web server, including per-core I/O wait measurements, to identify storage-related application slowdowns. ### Source excerpt If your Linux server is bogged down by disk I/O, your first step may often be to use the top command in the terminal to check load averages. Continue reading... ## Linux's IO\_uring Sees Some "Crazy" Patches That Deliver Very Nice Improvements DevFeed: [Linux's IO\_uring Sees Some "Crazy" Patches That Deliver Very Nice Improvements]() Original publisher: [Read original article]() Author: Michael Larabel Published: 2026-09-11T16:21:33Z Content type: news Language: en Sources: [Phoronix]() Topics: [io\_uring](), [Linux](), [IO]() Tags: [desktop-linux](), [developer](), [io](), [io-uring](), [kernel](), [linux](), [linux-benchmarking](), [linux-hardware-benchmarks](), [linux-hardware-reviews](), [linux-how-to](), [linux-performance](), [linux-server-benchmarks](), [open-source-graphics](), [phoronix](), [phoronix-test-suite](), [ubuntu-benchmarks](), [ubuntu-hardware]() ### AI overview Linux io_uring patches proposed by Jens Axboe aim to improve blocking behavior and reduce unnecessary thread wakeups, context switches, and task-work completion overhead. Requests would run inline in blocking mode and offload only when they actually block, while preserving the submitting task's user-visible identity. ### Source excerpt Linux block subsystem maintainer and IO_uring lead developer Jens Axboe continues to uncover new opportunities for making the wonderful IO_uring framework even more performant within the Linux kernel. For ending out the week he's posted some "crazy" patches delivering some significant gains... ## Evolving Pinterest's Embedding Retrieval Platform DevFeed: [Evolving Pinterest's Embedding Retrieval Platform]() Original publisher: [Read original article]() Author: Pinterest Engineering Published: 2026-09-11T15:01:03Z Content type: article Language: en Sources: [Pinterest Engineering Blog - Medium]() Topics: [Architecture & Design](), [Algorithms](), [Scalability](), [Latency](), [IO]() Tags: [ann](), [cost](), [embedding](), [embeddings](), [engineering](), [infrastructure](), [latency](), [memory](), [models](), [pinterest](), [platform](), [quantization](), [real-time](), [retrieval](), [scalability](), [search]() ### AI overview Pinterest describes evolving its Manas embedding-retrieval platform to address the cost, scale, and flexibility challenges of serving billions of embeddings. The excerpt covers ANN search, vector quantization, and SSD-based serving. ### Source excerpt Authors: Bowen Zhou | Staff Software Engineer; Shan Gao | Senior Software Engineer; Jingwen Hu | Software Engineer II; Wenjiang Chu | Staff Software Engineer The Billion-Embedding Challenge At Pinterest, the "signal" is our lifeblood. Whether it's a home decor enthusiast finding the perfect rug or a fashion seeker discovering a new aesthetic, our discovery engine relies on understanding deep semantic relationships to help our users find inspirations. Over the last few years, the explosive growth of embedding-based retrieval has fundamentally transformed how we surface these signals -- and at the heart of that transformation is Manas, Pinterest's in-house distributed search platform. Embedding Retrieval is one of the core capabilities of Manas, supporting multiple approximate nearest neighbor search algorithms, hybrid queries with both token and embedding clauses, as well as real-time updates to ensure fresh contents become searchable within seconds. Deployed on over 80 clusters and serving billions of embeddings, Manas embedding retrieval powers all major product surfaces at Pinterest including Home Feed, Search, Related Pins, Ads, and Notifications. However, as our corpus scales toward tens of billions of embeddings and our models capture increasingly complex interactions, we face mounting challenges around cost efficiency, scalability, and flexibility. On the infrastructure side, traditional ANN algorithms like HNSW are notoriously memory-hungry -- they require the entire index to reside in RAM to maintain low query latency, making cost grow linearly with corpus size. On the modeling side, the classic two-tower retrieval paradigm is too restrictive: it reduces each candidate to a single embedding and scores relevance through a simple dot product, leaving little room to express richer, context-dependent notions of similarity. To tackle these challenges, our team has been evolving Manas's embedding retrieval stack across three fronts: Quantization. We reduce the memor ## Python 3.15 Preview: UTF-8 by Default DevFeed: [Python 3.15 Preview: UTF-8 by Default]() Original publisher: [Read original article]() Author: Leodanis Pozo Ramos Published: 2026-09-02T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [IO](), [Operating system]() Tags: [linux](), [macos](), [python](), [release](), [tutorial](), [windows]() ### AI overview This tutorial previews Python 3.15's UTF-8 default for text I/O and explains how explicit encodings keep file handling portable across platforms and Python versions. ### Source excerpt Preview the Python 3.15 UTF-8 default: see what changes, try it on a pre-release, and keep your file I/O portable across every platform. ## Compute that takes any shape DevFeed: [Compute that takes any shape]() Original publisher: [Read original article]() Author: Luke Phillips-Sheard Published: 2026-09-01T07:00:00Z Content type: article Language: en Sources: [Vercel News]() Topics: [Cloud](), [Provisioning](), [cloud-infrastructure](), [Vercel](), [cpu](), [IO](), [DRIVE]() Tags: [agent](), [cloud](), [code](), [compute](), [cpu](), [image](), [infrastructure](), [io](), [memory](), [pricing](), [real-time](), [sandbox](), [storage](), [vercel]() ### AI overview Vercel presents Fluid as a unified compute system that assembles machines for different workloads, changes configuration dynamically, and absorbs burst capacity in real time. It supports builds, sandboxes, and functions, using isolated virtual machines, custom images, and separate storage connections. ### Source excerpt Workloads differ in how long they run, how much memory they take, and how much of the environment they control. That previously meant a different compute primitive for each job, so product iterations required provisioning infrastructure, not just working on a feature. We built Vercel so that none of this has to be your problem. The compute layer is a single system with a simple job. Take any workload, assemble the machine it needs, swap configuration on the fly, and absorb burst capacity in real time. We call this Fluid. Builds ran on it first, then sandboxes, and now functions. If you've shipped on Vercel, you've been on Fluid without knowing it. Fluid compute now runs over 15 million builds a day, 25 million sandboxes a week, and a trillion requests a month. A history of compute Changing a machine used to mean changing it by hand. You went down to the computer store, bought a hard drive or a stick of RAM, and swapped it in yourself. Then you could rent bare metal when you needed it, and use it to serve a website, host a database, or send email. Then the cloud let you request a machine in any configuration you wanted, choose its operating system, use it, and throw it away when you were done. For agents, even the cloud is too slow. A standard VM can't provision fast enough to keep up with how they work, but this is where Fluid excels. How a workload runs on Fluid When a workload comes in, Fluid assembles a machine to fit its shape. If an agent needs to run code, Hive provides an isolated VM, usually one already warm, so it's ready instantly. Your own image boots on top as the environment. A Drive connects, and your files are right where you left them, because storage was never tied to the machine. Each workload needs something different. A build is compute-bound, so it wants a beefy machine, heavy on CPU and memory. A function is IO-bound, loading specific code to run the instant a request lands, usually on a small VM. A sandbox is flexibility-bound, taking whatever ## Async Programming in Python: From Generators to asyncio DevFeed: [Async Programming in Python: From Generators to asyncio]() Original publisher: [Read original article]() Author: Doug Farrell Published: 2026-08-31T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Python](), [Programming](), [Concurrent Programming](), [IO](), [Code](), [Python 3.14]() Tags: [automation](), [batch](), [code](), [learning](), [programming](), [python](), [tutorial]() ### AI overview A tutorial on Python asynchronous programming, explaining how event loops, coroutines, async and await enable a single-threaded program to handle slow I/O without blocking. It progresses from synchronous code and generators to concurrent task execution with asyncio, using examples compatible with Python 3.11 and later. ### Source excerpt Learn how Python async programming works. Write async functions with async and await, and run slow I/O operations concurrently with asyncio. ## How Bits Database Optimization proves a query rewrite is faster DevFeed: [How Bits Database Optimization proves a query rewrite is faster]() Original publisher: [Read original article]() Author: Alex Weisberger; Nenad Noveljić; Bowen Chen Published: 2026-08-31T00:00:00Z Content type: article Language: en Sources: [Datadog | The Monitor blog]() Topics: [Optimization](), [database monitoring](), [Database](), [synthetic-data](), [datasets](), [cpu](), [IO](), [Security](), [Statistics]() Tags: [cpu](), [data](), [database](), [database-monitoring](), [infrastructure](), [optimization](), [performance](), [security](), [software](), [sql](), [statistics](), [synthetic](), [synthetic-data]() ### AI overview The article explains how Bits Database Optimization validates that a proposed query rewrite is faster. It describes controlled benchmarking with simulated production-like datasets, accounting for cache state, CPU and I/O contention, execution time, and database work. ### Source excerpt Learn how Bits generates synthetic data, measures simulation fidelity, and uses execution time and database work to determine whether an optimization is truly faster. ## Hot Chips 2026: Intel's Diamond Rapids DevFeed: [Hot Chips 2026: Intel's Diamond Rapids]() Original publisher: [Read original article]() Author: George Cozma Published: 2026-08-25T06:20:03Z Content type: article Language: en Sources: [Chips and Cheese]() Topics: [cpu](), [intel](), [Cache](), [IO](), [x86]() Tags: [2026](), [cache](), [cpu](), [intel](), [io](), [processor](), [x86]() ### AI overview The article examines Intel's Diamond Rapids server CPU, including its 3D-stacked L3 cache, die and packaging design, revised 256-core specification, 1.6 TB/s memory bandwidth, 128 PCIe Gen 6 lanes, and two new x86 ISA extensions. It compares these characteristics with AMD's Venice processor. ### Source excerpt Hello you fine Internet folks, ## Disrupting a new covert influence campaign from Russia DevFeed: [Disrupting a new covert influence campaign from Russia]() Original publisher: [Read original article]() Published: 2026-08-25T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [OpenAI](), [IO](), [ChatGPT]() Tags: [ai](), [articles](), [chatgpt](), [global-affairs](), [identity](), [linkedin](), [openai](), [social-media](), [telegram]() ### AI overview OpenAI describes disrupting a Russia-origin covert influence operation that used ChatGPT to generate social media comments promoting a fabricated Israel-based think tank and a Russia-favorable sovereignty index. The operators used VPNs and attempted to conceal their Russian origins. ### Source excerpt OpenAI banned Russia-origin accounts using AI to promote a fake Israel-based think tank and a "sovereignty" index praising Russia and criticizing the West. ## Native Async/Coroutine Reads in RocksDB DevFeed: [Native Async/Coroutine Reads in RocksDB]() Original publisher: [Read original article]() Author: Josh Kang Published: 2026-08-24T00:00:00Z Content type: release Language: en Sources: [RocksDB]() Topics: [rocksdb](), [Coroutines](), [C++](), [IO](), [API]() Tags: [async](), [asynchronous](), [blog](), [c-plus-plus](), [coroutine](), [io](), [native](), [rocksdb](), [thread]() ### AI overview RocksDB introduces experimental asynchronous Get and MultiGet APIs backed by native C++ coroutines. The APIs can suspend storage-bound reads, allowing a small executor to run other ready tasks and maintain more storage queue depth without one blocked application thread per read. The feature targets throughput for I/O-bound point lookups rather than reducing individual device-read latency. ### Source excerpt A point lookup that misses RocksDB's block cache can spend most of its time waiting for storage. The traditional way to keep more reads in flight is to add threads. That works, but each outstanding read parks a thread, carries a stack, and adds context-switching overhead. RocksDB now has experimental asynchronous Get and MultiGet APIs backed by native C++ coroutines. When a read reaches storage, RocksDB can suspend the request, let its read-executor worker run another ready task, and resume the request when the filesystem reports completion. A small executor can therefore maintain more storage queue depth without requiring one blocked application thread per read. These APIs are available in RocksDB 11.10.0. This is primarily a throughput feature for I/O-bound point lookups. It does not make an individual device read faster. Its benefit comes from keeping the device busy and using CPU threads for runnable work. The API surface RocksDB exposes the new read path through two public interfaces: DB::GetAsync and DB::MultiGetAsync return immediately on the native path and report completion through AsyncCallback::OnComplete. CoroDB::CoGet and CoroDB::CoMultiGet return lazy folly::coro::Task objects. CoGet produces a Status; CoMultiGet fills the same per-key values and statuses as synchronous MultiGet. The callback APIs suit applications that do not expose Folly tasks at their boundaries. The CoroDB APIs let coroutine-based callers await RocksDB directly, avoiding an application-side callback-to-Baton adapter and its extra completion handoff. Native execution requires RocksDB to be built with Folly and USE_COROUTINES=1. Neither interface requires ReadOptions::async_io. That flag continues to control the older internal async-I/O optimizations for synchronous MultiGet and iterators. The task APIs are lazy: no read begins until a task is awaited or started. Both interfaces take pointer and reference parameters, so keep the DB, column-family handles, ReadOptions, keys and their ## Linux Kernel Parameters Tuning for Better Performance DevFeed: [Linux Kernel Parameters Tuning for Better Performance]() Original publisher: [Read original article]() Author: Hayden James Published: 2026-08-22T21:03:32Z Content type: tutorial Language: en Sources: [Hayden James]() Topics: [Linux](), [Kernel](), [Server](), [IO](), [Latency]() Tags: [blog](), [how-to](), [kernel](), [latency](), [linux](), [linux-kernel](), [network](), [performance](), [server](), [sysadmins]() ### AI overview A practical guide to tuning Linux kernel parameters with sysctl and /proc/sys. It covers network throughput, memory management, file I/O, server performance, safe application of changes, persistence across reboots, and verification. It cautions that many defaults are sufficient and that TCP buffer increases mainly help bandwidth-limited, high-latency transfers. ### Source excerpt Learn how to tune Linux kernel parameters using sysctl and /proc/sys for better network throughput, memory management, file I/O, and server security. Includes a ready-to-use baseline config. Continue reading... ## POSETTE Talk Recap - Postgres Isn't Slow. Your Storage Is DevFeed: [POSETTE Talk Recap - Postgres Isn't Slow. Your Storage Is]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-08-20T16:11:08Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [PostgreSQL](), [benchmarking](), [IO](), [Latency](), [Replication](), [systems](), [clickhouse](), [Kafka]() Tags: [benchmark](), [clickhouse](), [cpu](), [kafka](), [latency](), [local](), [performance](), [postgres](), [postgresql](), [replication](), [storage](), [systems]() ### AI overview This article recaps a POSETTE 2026 talk about how storage performance affects PostgreSQL workloads at scale. It describes a benchmark comparing a 3.3-billion-row workload on gp3 EBS and local NVMe, covering ingestion, read latency, autovacuum, checkpoints, and logical replication, and introduces production patterns for running PostgreSQL on local NVMe. ### Source excerpt A recap of Sai Srirampur's POSETTE 2026 talk on storage performance in Postgres, with a local NVMe vs. EBS benchmark and the production setup behind it. ## v20.2.3 Tentacle released DevFeed: [v20.2.3 Tentacle released]() Original publisher: [Read original article]() Author: Yuri Weinstein Published: 2026-08-05T00:00:00Z Content type: release Language: en Sources: [Ceph Blog]() Topics: [Deployment](), [Server](), [Hardware](), [TLS (Transport Layer Security)](), [Kafka](), [Grafana Cloud Metrics](), [Monitoring](), [Command-line interface](), [IO]() Tags: [2026](), [authentication](), [blog-post](), [cli](), [en-article](), [en-blog-post](), [hardware](), [io](), [kafka](), [metrics](), [monitoring](), [release](), [storage](), [tentacle](), [tls]() ### AI overview Ceph v20.2.3, the third minor release in the Tentacle series, is a recommended update containing fixes and improvements across metadata servers, object storage daemons, RADOS Gateway, client libraries, the dashboard, ceph-volume, and NVMe-oF. Changes include storage reliability fixes, TLS 1.3 cipher customization, encrypted object copying, Kafka notification authentication, hardware monitoring, CSV host uploads, and role-management updates. ### Source excerpt This is the third minor release in the Tentacle series. We recommend that all users update to this release. Release Date ¶ August 5, 2026 Notable Changes ¶ MDS (Metadata Server) ¶ Fixed a crash and shutdown hang that could occur when ephemeral pins were active and max_mds was set to 0. Session reclaim could miss blocklisting an old session; this is now handled correctly. Fixed a case where scrub was unable to identify damage from an irreparable hard link. OSD (Object Storage Daemon) ¶ BlueStore: Fixed a BlueFS WAL envelope-mode bug that caused write buffer misalignment. Erasure Coding: Fixed truncate+write planning for EC shard sizes. Fixed a condition that prevented rolling forward of PG log entries. Prevented OSDMap::check_health() from asserting when new OSDs are found in a subtree. Scrub: "repairing" scrubs -- deep scrubs re-run specifically to fix a previously detected inconsistency -- are no longer blocked by the configured allowed scrub hours/days; they can now run at any time. RGW (RADOS Gateway) ¶ Added the ssl_ciphersuites option for the Beast front-end to support TLS 1.3 cipher customization. Fixed PutObject's canned_acl comparison bug affecting BlockPublicAcls. Fixed crashes occurring on realm reload. Implemented CopyObject support for encrypted objects. Added mTLS client-certificate authentication support for Kafka notifications. Fixed removal of delete markers in lifecycle processing. RADOS / librados / neorados ¶ neorados: Fixed an infinite trim loop on empty data log shards. Fixed cancellation-slot cleanup in librados/asio's associated executor. Dashboard ¶ Added a hardware-monitoring dashboard backed by node-proxy metrics. Fixed a bind-address regression caused by CherryPy isolation. Added support for adding hosts via CSV upload. NVMeoF: multiple CLI and UI backports (EC pool support, IO statistics, role management). Object: The global RGW Roles tab has been removed. Role management is now scoped under RGW Accounts, where roles can be listed, created, ## Postgres backups under the hood DevFeed: [Postgres backups under the hood]() Original publisher: [Read original article]() Author: Josh Brown Published: 2026-07-24T00:00:00Z Content type: article Language: en Sources: [Blog -- PlanetScale]() Topics: [Databases](), [data](), [SQL](), [IO](), [Filesystems]() Tags: [backup](), [data](), [database](), [engineering](), [how-to](), [io](), [postgres](), [recovery](), [sql]() ### AI overview This article explains how Postgres backups work, focusing on logical backups and their tradeoffs. It describes consistent snapshots, SQL or custom output formats, resource costs at large data volumes, the lack of point-in-time recovery, and the use of MVCC for online backups. ### Source excerpt With backups being such a vital part of keeping your data safe, how do they actually work? ## Offloading I/O to Dedicated Cores: An Asymmetric io\_uring Backend for Seastar and ScyllaDB DevFeed: [Offloading I/O to Dedicated Cores: An Asymmetric io\_uring Backend for Seastar and ScyllaDB]() Original publisher: [Read original article]() Author: Cynthia Dunlop Published: 2026-07-22T18:03:10Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [IO](), [io\_uring](), [Architecture & Design](), [C++](), [distributed-systems](), [Linux](), [networking](), [Latency]() Tags: [aio](), [architecture](), [c-plus-plus](), [cpu](), [design](), [engineering](), [i](), [io-uring](), [latency](), [linux](), [networking](), [performance](), [seastar](), [systems]() ### AI overview This article presents Seastar's asymmetric io_uring backend, which moves low-level I/O work from application cores to dedicated cores. It explains the shared-nothing architecture, the motivations for offloading I/O, the design trade-offs, and the comparison with Seastar's existing linux-aio backend. ### Source excerpt We moved low-level I/O execution off application cores to dedicated networking cores using Seastar's new asymmetric_io_uring backend. Explore the architecture design, trade-offs, and benchmark results. ## libosmocore - libosmocore 1.14.1 released DevFeed: [libosmocore - libosmocore 1.14.1 released]() Original publisher: [Read original article]() Author: osmith Published: 2026-07-22T10:37:33Z Content type: release Language: en Sources: [Open Source Mobile Communications: News]() Topics: [changelog](), [IO](), [cpu](), [scheduling](), [make]() Tags: [2026](), [bug](), [changelog](), [cpu](), [io](), [issue](), [make](), [scheduling](), [tracker]() ### AI overview libosmocore 1.14.1 has been released with fixes to I/O queue handling, CPU scheduling initialization and platform-dependent scheduling support, utility issues, and a missing crypto header in the build configuration. ### Source excerpt Today we've released libosmocore 1.14.1. The changelog over 1.14.0 is as follows: libosmocore (1.14.1) unstable; urgency=medium [ Vadim Yanitskiy ] * osmo_io: iofd_txqueue_dequeue(): fix double next->io_len decrement [ Timur Davydov ] * vty: fix osmo_cpu_sched_vty_init() prototype * vty: make CPU scheduling optional based on platform support [ Oliver Smith ] * utils: fix typos * utils/gsmtap-logsend: close infile before exit [ Alexander Couzens ] * gsm/Makefile.am: add missing kdf/crypto.h -- Oliver Smith Wed, 22 Jul 2026 11:30:55 +0200 ## Top System Design Performance Metrics DevFeed: [Top System Design Performance Metrics]() Original publisher: [Read original article]() Author: Saurabh Dashora Published: 2026-07-14T08:36:41Z Content type: tutorial Language: en Sources: [System Design Codex]() Topics: [benchmarking](), [Availability](), [Architecture & Design](), [Scalability](), [Load Balancing](), [health checks](), [Concurrency](), [Disaster Recovery](), [Database](), [sharding](), [IO](), [API]() Tags: [api](), [availability](), [blocking](), [concurrency](), [database](), [disaster-recovery](), [health-checks](), [load-balancing](), [performance](), [performance-metrics](), [scalability](), [sharding](), [system-design]() ### AI overview A tutorial on system design performance metrics, focusing on availability and throughput. It explains how these metrics are measured and outlines techniques such as load balancing, health checks, failover, redundancy, disaster recovery, query optimization, sharding, and asynchronous processing. ### Source excerpt Must Know Metrics ## Mastering Linux Administration: 20 Powerful Commands to Know DevFeed: [Mastering Linux Administration: 20 Powerful Commands to Know]() Original publisher: [Read original article]() Author: Hayden James Published: 2026-07-06T03:56:38Z Content type: tutorial Language: en Sources: [Hayden James]() Topics: [Linux](), [Command-line interface](), [Monitoring](), [Filesystems](), [IO](), [cpu]() Tags: [administration](), [automated](), [backup](), [blog](), [command-line](), [commands](), [cpu](), [examples](), [files](), [https-linuxblog-io-tag-commands](), [io](), [linux](), [memory](), [monitoring](), [ncurses](), [sysadmins]() ### AI overview A tutorial introducing 20 Linux commands for system administrators, with examples and explanations of their uses. The supplied text specifically discusses vmstat for monitoring system and memory statistics, inotifywait for tracking filesystem events, and ncdu for analyzing disk usage. ### Source excerpt Linux is a powerful and versatile operating system that can be used for a wide range of purposes. For many system administrators, the command line interface is the primary way to interact with the system. Continue reading... ## Designing GPU-Accelerated Query Engines with NVIDIA GQE DevFeed: [Designing GPU-Accelerated Query Engines with NVIDIA GQE]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-06-30T17:36:43Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [GPU](), [Databases](), [NCCL](), [CUDA](), [NVLink](), [Compression](), [IO](), [SQL](), [Parser](), [Streaming](), [Hardware](), [Open Source]() Tags: [accelerate](), [compression](), [cpu](), [cuda](), [cuda-x](), [data](), [data-analytics-processing](), [data-center-cloud](), [data-science](), [databases](), [featured](), [gpu](), [hardware](), [memory](), [nvidia](), [nvlink](), [performance](), [sql]() ### AI overview This article presents GQE, a reference architecture for executing SQL queries on GPUs. It explains how NVIDIA hardware and CUDA-X libraries address memory, I/O, data movement, decompression, and end-to-end performance challenges for large datasets. ### Source excerpt GPU-accelerated query engines are often constrained by memory and I/O bandwidth. NVIDIA hardware advances--including high bandwidth memory (HBM), NVIDIA... ## How ScyllaDB's Trie-Based Index Delivers Up to 3X More Throughput DevFeed: [How ScyllaDB's Trie-Based Index Delivers Up to 3X More Throughput]() Original publisher: [Read original article]() Author: Tzach Livyatan Published: 2026-06-30T13:03:08Z Content type: article Language: en Sources: [ScyllaDB]() Topics: [benchmarking](), [Benchmark](), [Latency](), [Optimization](), [Caching](), [IO](), [Seastar](), [2026.2](), [Apache Cassandra]() Tags: [2026-2](), [apache](), [architecture](), [benchmark](), [benchmarking](), [cache](), [cassandra](), [deep-dive](), [efficiency](), [engineering](), [latency](), [memory](), [optimization](), [performance](), [product](), [seastar](), [storage](), [time]() ### AI overview ScyllaDB's Trie-based SSTable index replaces separate summary and index files with a prefix tree. The article explains the format change, its storage layout and lookup behavior, and reports benchmark results showing 30% to 230% higher throughput and 31% to 63% lower latency than legacy indexes across four read workloads. ### Source excerpt By transitioning from separate summary and index files to a prefix tree, we optimized cache efficiency, reduced disk I/O, and reduced memory overhead ## Real-Time CPU and Memory Insights for Harness CI Cloud Build DevFeed: [Real-Time CPU and Memory Insights for Harness CI Cloud Build]() Original publisher: [Read original article]() Author: Abhay Ganvir Published: 2026-06-17T00:00:00Z Content type: article Language: en Sources: [Harness Blog]() Topics: [ci](), [build performance](), [Cloud](), [cpu](), [IO](), [real-time]() Tags: [build](), [build-performance](), [ci](), [cloud](), [code](), [cpu](), [infrastructure](), [performance](), [real-time]() ### AI overview This article introduces CPU and Memory Insights for Harness CI Cloud builds. The feature collects CPU, memory, and disk I/O metrics from ephemeral build machines, streams them to the Harness platform in real time, and displays historical data to help teams right-size machines, investigate out-of-memory failures, and detect performance regressions. ### Source excerpt Get real-time CPU, memory, and disk I/O insights for Harness CI Cloud builds. Right-size machines, debug OOMs faster, detect regressions, and optimize CI performance with zero setup. | Blog [Next page]()