I'd recommend "A Primer on Memory Consistency and Cache Coherence, Second Edition"
- Synthesis Lectures on Computer Architecture 15:1 (2020)
- Vijay Nagarajan, Daniel J. Sorin, Mark D. Hill, David A. Wood
- (open access, freely available): https://doi.org/10.2200/S00962ED2V01Y201910CAC049
"We've been thinking about a lot about the language design of expressing lifetime restrictions and dependencies for non-escapable types in Swift, and I put all that into a long and somewhat rambling design paper which folks might find interesting."
- vllm-sr/auto: efficient, fast, balanced routing, similar in spirit to Fugu // Sakana Fugu — Multi-Agent System as a Model: https://sakana.ai/fugu/
- vllm-sr/fusion: panel-style multi-model reasoning and synthesis.
- vllm-sr/flow: router-native workflow orchestration
- vllm-sr/remom: multi-round reasoning over one or multiple models.
Good questions. From what I can tell, vLLM semantic router is more optimized for one-off prompt/response workflows rather than agentic coding (I don't think it's cache aware).
As another commenter (https://news.ycombinator.com/item?id=48689994) pointed out, for one-off requests, I think it makes more sense to lock to one model whose behavior you understand very well. For dynamic requests like the ones going to a coding agent I think dynamic routing makes more sense but it does need to be cache aware.
I tried Sakana Fugu, boy is it hungry ... it blows up tokens like nothing I have ever seen. Not that impressed with the results I got from it however if I'm being honest. Now I'm bought into their buy 1 get 2nd month free so will keep trying it but may cancel after.
AutoParallel: a PyTorch library that automatically shards and parallelizes models for distributed training. Given a model and a device mesh, it uses linear programming to find an optimal sharding strategy (FSDP, tensor parallelism, or a mix) and applies it — no manual parallelism code required.
> AutoParallel is a PyTorch library that automatically shards and parallelizes models for distributed training. Given a model and a device mesh, it uses linear programming to find an optimal sharding strategy (FSDP, tensor parallelism, or a mix) and applies it — no manual parallelism code required.
> Despite its high-performance capabilities, C++ is not the first programming language that comes to mind for rapidly developing robust applications, mainly due to the long edit-compile-run cycles. Ongoing research in the compiler-research.org group aims to provide practical, interactive capabilities for C++, enabling dynamic interoperability, rapid prototyping, and exploratory programming, essential for data science and other applications. This talk explores how interactive C++ can be leveraged for various scientific usecases and teaching. Attendees will also learn how to leverage Clang as a library to build a simple C++ REPL for incremental compilation and introspection, integrating this layer with the Python runtime.
> The second part of this talk covers CppInterOp, a production-grade C++ interoperability library based on LLVM and Clang that provides compiler-as-a-service capabilities for seamless cross-language integration. CppInterOp formalizes a stable, backward-compatible API that enables dynamic languages to harness the full power of modern C++ without sacrificing expressiveness or performance. We explore applications of the CppInterOp library in the context of Python/C++ bindings, interactive C++ notebooks with xeus-cpp, and WebAssembly.
For a layperson it's clear that it's either "Writings" and "Talks", or "Readings" and "'Listenings", but CPP profeciency is in an inverse relation with being apt in taxonomy, it looks like.
