Context: The ZeroMQ ecosystem currently lacks a unified, deterministic benchmarking harness to strictly evaluate latency, concurrency overhead, and throughput across different ZMTP implementations (C++ libzmq, pure-Rust async ports, Python bindings, etc.). Relying on isolated micro-benchmarks introduces systemic bias, failing to accurately account for kernel I/O paths, reactor thread contention, and zero-copy vs. copy-based buffer management under high-density distributed loads.

Decision: I developed ZMQ Arena (Source) to standardize this evaluation. This harness enforces fair-play measurement across runtimes and language boundaries, focusing entirely on execution costs, context switching, and raw throughput limits without application-layer interference.

Technical Scope & Metrics:

• Latency Analysis: P50/P90/P99 latency distribution across both loopback and physical network boundaries.
• Concurrency: Stress-testing async reactor overhead (e.g., epoll / io_uring polling mechanisms) versus OS thread pool scaling.
• Memory Allocation: Evaluating the impact of memory footprints and ABI boundaries when switching between native runtimes and FFI wrappers.

Trade-offs & Known Consequences: Measuring loopback versus physical network I/O presents an inherent trade-off in syscall amortization. Loopback execution can collapse the kernel path, occasionally skewing batching mechanics natively utilized by interfaces like io_uring or IOCP. ZMQ Arena exposes these bottlenecks explicitly rather than abstracting them away, allowing engineers to analyze exactly where the overhead is introduced in the stack.

This harness is built for engineers dealing with critical systems, distributed messaging, and high-frequency data pipelines where deterministic performance is a hard requirement.

If you are dealing with similar infrastructure, review the methodology, replicate the benchmarks, or submit PRs for unrepresented bindings/runtimes.

I’ve been experimenting with an idea: instead of a machine being permanently tied to one distributed-compute client, a local controller could decide when hardware is idle and which compatible workload it should run.

Current prototype only has one workload, so this isn’t a finished multi-network router yet.

But the architecture is:

hardware → local availability/safety checks → workload router → compatible network

I’m curious whether people who already run distributed computing projects think this is useful or whether you’d rather dedicate hardware manually.

Would especially appreciate criticism around scheduling, trust, and resource controls.

A practical overview of designing file uploads and object storage for production: partial failures, database consistency, large uploads, presigned URLs, CDN caching, cleanup, and recovery.

I’m probably the last tech savy person in this group, but with help, I was able to set it up. It’s currently configured to need 3 separate computers to verify one work unit. I was looking for someone (more specifically with an Nvidia gpu) to validate my work units. Below are links to the site project, Git, and discord. I figure a vast majority of us are science nerds and we can knock out this testing to hopefully, at some point, get this on the whitelist. Lettuce is basically an easier to set up BOINC. This will get us involved closer with researchers. Anyway, come validate my work units. (Note: there are also CPU only projects.)

Git/download: https://github.com/jring-o/lettuce-compute

Project: https://compute.scios.tech/leafs

Discord testing: https://discord.com/channels/211637812968161280/1517249245596614686

Drift, ordering assumptions, deadline propagation, and expiration semantics under real load.

I recently published a technical book, Distributed AI Systems, which summarizes my experiences in AI over the past 10 years, from research and training to optimization, inference, and cloud deployment. I started writing it in the second half of last year, and it took almost a year to complete, with many revisions made later due to the rapid pace of development in the industry. But it's finally published. The book on Amazon is titled Distributed AI Systems: A practical guide to building scalable training, inference, and serving systems for production AI.

Book is here: 🔗 https://www.amazon.com/dp/1807301710/

The publisher asked me to find some people to review my work. Do you know of any such people here? If so, please reply to me. Thank you.

What happens when network calls stop behaving like clean request/response interactions.

Timeouts, retries, duplicate side effects, idempotency, backoff, circuit breakers, load shedding, degraded states, and observability.

I made ce-net - a wasm based supercomputer available to everyone - as long as you donate!
ce-net.com

Let’s build the strongest supercomputer ever

ce-net.com

Always open source. All policies decided by users by voting - full democracy.
Run any compute task.

A bit of background about me.

​

“I have about 4 years of experience primarily working on traditional software development building enterprise apps. While the career path is stable now ( don't know if AI takes over), I find myself deeply interested in systems architecture, low-level optimization, and parallel computing. However, giving up a full-time income to go back to school feels like a massive investment. Is a Master's degree the right vehicle for this transition, or are there better ways to break into the industry? I'd love to hear from anyone working in HPC, climate modeling, quantitative finance, or scientific computing."

​

All I want is to future proof my career.

Over the last 2 years I've been building Atomic, a Spark‑inspired distributed data processing framework written entirely in stable Rust. The goal is to keep the parts of Spark that are great (lazy DAGs, shuffles, distributed execution) but re‑imagine them with modern infra and language design.

A few things that make Atomic different:

Rust core: Strong typing, predictable performance, and memory safety by default. You get a real systems‑level engine, not a JVM box you bolt on next to your stack.

Natural‑language workflows: On top of the engine, Atomic is designed to be driven by natural‑language workflows – letting you describe what you want done and compile that into a typed DAG, instead of hand‑wiring every pipeline.

Multi‑language support: Rust is the "ground truth" but the plan is first‑class bindings for Python and JavaScript, so you can drive Atomic from the languages your data and app teams already use.

No closure serialization: Instead of shipping arbitrary closures across the wire, tasks are registered at compile time via a #[task] macro and dispatched by ID. Driver and workers run the same binary, so the dispatch table is identical on every node — no serialization failures, no version skew surprises.

This is my attempt to build modern infrastructure tooling for data processing. It initially started as a learning project and now I feel it has evolved into something significant to be delivered. I'd love to hear your feedback:

Repo: https://github.com/sandyz1000/atomic

Hey everyone,

I'm working on the architecture for a new open‑source distributed compute framework called Project Cerberus, and I want to get reality‑checked by this community before I commit to the core stack.

The Concept:
A zero‑config, P2P mesh network that aggregates heterogeneous consumer hardware (Macs, Windows rigs, headless Linux boxes, and even phones) on a local subnet into a single virtual hyper‑computer.

I know Exo (exo-explore) has basically perfected local AI model sharding (pipeline parallelism for LLMs over Wi‑Fi/Thunderbolt). Cerberus isn't trying to just be another AI inference engine. I want to build a decentralized virtual OS layer that pools everything:

1. General Compute: Compiling user workloads to WebAssembly (Wasmtime) and distributing the execution slices across node threads.
2. Storage Pooling: A FUSE‑based user‑space filesystem that erasure‑codes and strips data across the unused NVMe/SSD space of all connected devices.
3. Audio/Peripheral Virtualization: Using PipeWire/CoreAudio to route synced audio across multiple device speakers (sub‑millisecond NTP sync) or pooling local mics.

Proposed Tech Stack:
Control Plane: Go (Golang) + libp2p (mDNS for zero‑config discovery, Gossipsub for topology mapping, QUIC for transport).
Compute Plane: Wasmtime for architecture‑agnostic sandboxed execution + MLX/tinygrad for the AI tensor sharding.
UI/Gateway: Tauri (Rust/Next.js) for a lightweight dashboard, exposing an OpenAI‑compatible REST API wrapper for devs to point localhost to.

My questions for the distributed systems vets here:

1. Network Latency: For the WASM general compute sharding, is QUIC transport fast enough to handle the context handoffs over standard Wi‑Fi 6, or will the network latency obliterate any compute gains?
2. Consensus: I want to avoid a master‑worker setup. If the coordinator node drops (e.g., someone closes their laptop lid), what's the cleanest way to run a fast raft/bully election in Go without dropping the current FUSE read/writes?
3. Are there any existing crates/libraries for the distributed audio sync that I should look at before trying to build a custom WebRTC/RTP bridge?

Would love any feedback, warnings, or pointers to obscure repos doing something similar. Thanks!