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mattpd

@mattpd@mastodon.social
mastodon 4.8.0-nightly.2026-10-06
  • Open on mastodon.social

https://github.com/MattPD
https://twitter.com/matt_dz

0 Followers
0 Following
11 Posts
Joined April 20, 2017
Open post
mattpd @mattpd@mastodon.social
· 1mo ago

mold: A Massively Parallel Linker
https://arxiv.org/abs/2608.23228
Rui Ueyama
International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) 2027

arxiv.org
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Open post
mattpd @mattpd@mastodon.social
· 1mo ago

Lost Bytes At The Crossroads Between User- And Kernel-Level Memory Allocation
https://www.ibr.cs.tu-bs.de/vss/Publications/2026/fistanto_26_lost_bytes.pdf
Pasha Fistanto, Sören Tempel, and Christian Dietrich
14th Workshop on Programming Languages and Operating Systems (PLOS) 2026

ibr.cs.tu-bs.de
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Open post
mattpd @mattpd@mastodon.social
· 7mo ago

When AI Writes the World’s Software, Who Verifies It?

https://leodemoura.github.io/blog/2026/02/28/when-ai-writes-the-worlds-software.html

"Writing a specification forces clear thinking about what a system must do, what invariants it must maintain, what can go wrong. This is where the real engineering work has always lived. Implementation just used to be louder."

leodemoura.github.io
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Open post
mattpd @mattpd@mastodon.social
· 5mo ago
Replying to

@TomF@mastodon.gamedev.place On that note, also like John Ousterhout's "Always measure one level deeper", https://al.radbox.org/doi/10.1145/3213770

"If you want to understand the performance of a system at a particular level, you must measure not just that level but also the next level deeper. That is, measure the underlying factors that contribute to the performance at the higher level."

al.radbox.org

Always measure one level deeper

Performance measurements often go wrong, reporting surface-level results that are more marketing than science.

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Open post
mattpd @mattpd@mastodon.social
· 8mo ago
Replying to
@neilhenning@mastodon.gamedev.place @nikic@mastodon.social Somewhat related (a different ecosystem, albeit MLIR-based) but this looks like an interesting idea: Triton Extensions: a framework for developing and building Triton compiler extensions https://github.com/triton-lang/triton-ext/ Presented a few days ago: https://www.youtube.com/watch?v=JnFFwBB6Dhk&t=781s Allows to develop custom passes, dialects, backends, and language extensions downstream without having to fork the upstream compiler codebase.
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Open post
mattpd @mattpd@mastodon.social
· 6mo ago
Replying to
@glennklockwood BTW, these findings look interesting: https://specdecode-bench.github.io/
specdecode-bench.github.io

Speculative Decoding: Performance or Illusion? | SpecDecode-Bench

The first systematic study of speculative decoding on a production-grade inference engine (vLLM), covering multiple SD variants across diverse workloads, model scales, and batch sizes.

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Open post
mattpd @mattpd@mastodon.social
· 6mo ago
Replying to
@adrian@discuss.systems Thanks! BTW, am I the only one getting "Abstract: Data missing." with https://al.radbox.org/doi/10.1145/3779212.3790241 It does show up on https://dl.acm.org/doi/10.1145/3779212.3790241 FWIW
al.radbox.org

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

Memory access errors remain one of the most pervasive bugs in GPU programming. Existing GPU sanitizers such as compute-sanitizer detect memory access errors by instrumenting every memory instruction in low-level IRs or binaries, which imposes high overhead and provides minimal memory access error diagnostic context for fixing problems. We present Triton-Sanitizer, the first device-agnostic memory sanitizer designed for Triton, a domain-specific language for developing portable, efficient GPU ker

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Open post
mattpd @mattpd@mastodon.social
· 6mo ago
Replying to
@adrian@discuss.systems Ah, not just me, then! I'm blaming them wild ASPLOS folks taking ideas like the Asemantic Code Revolution too far! Thanks for the service in any case, this is a vast improvement over the ACM DL "experience" :-)
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Open post
mattpd @mattpd@mastodon.social
· 6mo ago
Replying to
@glennklockwood See also Speculative Speculative Decoding, https://arxiv.org/abs/2603.03251 https://github.com/tanishqkumar/ssd
arxiv.org
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Open post
mattpd @mattpd@mastodon.social
· 1mo ago

Architecting the Next Generation of Asynchronous, Distributed GPUs for the AI Era
https://arxiv.org/abs/2608.22602
Accel-Sim 2.0: Validated GPU Simulation with full Hopper support
https://github.com/accel-sim/accel-sim-framework

arxiv.org
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Open post
mattpd @mattpd@mastodon.social
· 6mo ago
Replying to
@adrian@discuss.systems That works, thanks!
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