PI

PIM Programming in Spiral: Upmem Demo

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PIM Programming in Spiral: Upmem Demo

Here is a demo of a backend for a Process-In-Memory chip. It is a demonstration of Spiral's ability for heterogeneous programming. I want to do more backends for different kinds of devices, and posting this on HN to gauge the interest in this kind of work. While x86 devices have a ton of good languages, new and niche hardware is likely to have poor language support and tooling, so I am hoping to fill that gap with Spiral. Whether you are a company that has some new kind of hardware or a user of it and you are fatigued of programming it in C, that is what I aim to alleviate. Going from a low level language like C to a high level one like Spiral would significantly boost your productivity. The backend I am demoing here is for UPMEM, but now that I have it as a template, I could easily make similar backends for other kinds of devices assuming I have access to at least a simulator.

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
73%73% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
42%42% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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