Jean-Philippe Bempel @jpbempel.bsky.social

Sr Sw Eng @Datadoghq, optimize software running on JVM. Love performance topics and mechanical sympathy supporter. Java Champion & @javamissionctrl committer

[Jean-Philippe Bempel @jpbempel.bsky.social @jpbempel]

New blog post: Chasing a Metaspace leak

#OpenJDK #Metaspace #MemoryLeak #Instrumentation
https://t.co/R9WhVOzh0D

[Francesco Nigro @forked_franz]

After few days studying how go and other runtimes implements work-stealing the Loom custom scheduler - I've built a new one for @netty_project (but can be used with any transport you like as #aeron or #dpdk..) on locality-first principles, see https://t.co/KCbhK7a7Px (1/2)

[Vivek Galatage @vivekgalatage]

Concurrency in C++: A Programmer’s Overview by Fedor Pikus is a great two-part talk.

https://t.co/4z3CjWcNuz

[Mark E. Dawson, Jr. @medawsonjr]

https://t.co/1hshymjcmx

[Vivek Galatage @vivekgalatage]

Memory organization with Algorithmica is one resource that keeps shining.

https://t.co/hvIj8m3S5l

[Erik Gahlin @ErikGahlin]

https://t.co/9PCurbfd5R

[Cliff Click @cliff_click]

https://t.co/tHQXzHJdCv
AI sandboxing. Simple restructure. Lists vs vectors, byte slices, CallGraph in SeaOfNodes, LTO, repl, separate compilation. Literals: dict, array, XTC. JSON f64, i64, NaN. Twitter war story. String formatting. Overloading, type classes, ambiguous defs

[Aleksey Shipilëv @shipilev]

🎉Shenandoah Late Barrier expansion is in! https://t.co/yGA9tOnISp

Comes with great throughput improvements. Unblocks Valhalla support. Opens up more advanced optimization and cleanup opportunities. @rkennke and @JohnTortugo are MVPs!

[John Rose @JohnRose00]

Here is a deep dive from Netflix about using #Java AOT caches (from Project #Leyden). Big kudos to Martin Chalupa and Ian Brown for putting the parts together, and explaining how it works at #JavaOne. https://t.co/xXd7m0XqFV

[Cliff Click @cliff_click]

https://t.co/4vHDSD54AM
Clang compile speed, constexpr interpreter. Recursive types & type inference. User peephole opts, heroic opts, semantics. Infinite loop opts, Collatz Conjecture, UB. Research vs production compiler

[Vivek Galatage @vivekgalatage]

MIT's Performance Engineering of Software Systems course is fantastic.

Topics include perf analysis, algorithmic techniques for high perf, instruction-level & caching optimizations, parallel programming, and more.

https://t.co/l3UszCiGw2

[Ionut Balosin @ionutbalosin]

After a year of daily AI-assisted coding, I wrote down what actually works - spec-driven dev, skills as guardrails, incremental verification.

https://t.co/s0fUxd5bLr

AI is an amplifier. It magnifies discipline and chaos with equal enthusiasm.

#AI #SoftwareEngineering

[JEP Updates @Jepupdates]

JEP 523 ("Make G1 the Default Garbage Collector in All Environments") updated: Status changed from "Proposed to target for version 27" to "Targeted for version 27". See https://t.co/s7lz75PgP5

[JEP Updates @Jepupdates]

JEP 534 ("Compact Object Headers by Default") updated: Status changed from "Proposed to target for version 27" to "Targeted for version 27". See https://t.co/bubhuqMuwB

[Ionut Balosin @ionutbalosin]

🚀 G1 GC: 5-15% Performance Gains with Dual Card Tables

Read more: https://t.co/JLuF0u9esR

#Java #Performance #GarbageCollection #JVM #JDK26

[Roman Kennke 🗽 @rkennke]

JEP 534: Compact Object Headers by Default
"Hearing no objections, I’ve targeted this JEP to JDK 27."
🥳🎉🙌

https://t.co/p1YZcRgETs

[Vivek Galatage @vivekgalatage]

Pair the article with this amazing book - Algorithms for Modern Hardware, for detailed insights on various interesting topics!

https://t.co/7CVZrs4X7A

[Cliff Click @cliff_click]

https://t.co/o3f8m2E3M1
AST, Sea of Nodes, V8, C2, SSA-CFG. Ruby IR, opts in parser, inlining, value profiling, Ruby JIT in Rust. CFG & mem aliasing. V8 vs Maglev vs SofN, More & more inlining, LTO, LLVM. SofN - philosophy. Type lattice in pictures, deep dive type impl

[Abhinav Upadhyay @abhi9u]

Before jumping into books like DDIA or Database Internals, it helps to understand the systems layer these designs are built on.

A lot of the design of such data-intensive systems is based on virtual memory: page tables, page faults, mmap, the page cache, swapping, NUMA placement, TLBs, and the tradeoffs between what the OS wants and what the database wants.

My latest article is a ~25,000-word mini-book on virtual memory.

It starts from first principles and goes all the way down to advanced topics like NUMA placement and performance debugging with tools like perf and /proc.

I also wrote it differently: as a dialogue between a user-space process and the kernel.

Most treatments of virtual memory are dry and fact-heavy. I wanted this one to feel more like a story, while still being technically deep.

Link below.

[Jean-Philippe Bempel @jpbempel.bsky.social @jpbempel]

@nevgeniev @Jepupdates Comparing G1 to ZGC/Shenandoah is unfaithful IMHO. The trade offs are completely different. Low latency GCs like ZGC/Shenandoah target to have a minimal sub millis pauses, while G1 is more about throuput targeting max 200ms pause.

[Aleksey Shipilëv @shipilev]

I mean, look at this: there is hardly a sub-workload where scores do not improve. The improvements like these are as rare as unicorns. It is likely to be once in a lifetime thing.