301–310 of 432 posts
I am ex-core contributor Chromium and Node.js and current core contributor to gRPC Core/C++. I am never bothered with build times. There is "interactive build" (incremental builds I use to rerun related unit tests as I work on code) and non-interactive build (one I launch and go get coffee/read email). I have never seen hardware refresh toggle non-interactive into interactive. My personal hardware (that I use now and…
Earlier quoted context omitted.
This might have to do with scale. At my employer (~7k employees) we started down this path a few years ago as well, and while it has taken longer for remote to be better than local, it now definitively is and has unlocked all kinds of other stuff that wasn't possible with the local-only version. One example is working across multiple branches by switching machines instead of files on local has meant way lower latency…
Are you using a public cloud to host the dev boxen? Is compilation actually faster than locally – assuming that your pc's having been replaced to lower-specced versions since they don't do any heavy lifting anymore? I work for a not-really-tech company (and I'm not a full-time dev either), so I've been issued a crappy "ultra-portable" laptop with an ultra-low-voltage CPU. I've looked into offloading my dev work to an…
I’ve been integrating psrecord into our builds to track core utilisation during the built and see that a lot of time is spent in single threaded activities. Effort is required to compile modules in parallel but that is actually quite straightforward. Running all tests in parallel is harder.
We get the most out of the cloud machines by being able to provision a 16+ core machine to run more complicated (resilience) tests and benchmarks.
Also note that typically the cloud machines run on lower clocked CPUs than you would find in a workstation depending on which machine you provision.
This is a great write-up and I love all the different ways they collected and analyzed data. That said, it would have been much easier and more accurate to simply put each laptop side by side and run some timed compilations on the exact same scenarios: A full build, incremental build of a recent change set, incremental build impacting a module that must be rebuilt, and a couple more scenarios. Or write a script that…
I didn't see any analysis of network building as an alternative to M3s. For my project, ~40 million lines, past a certain minimum threshold, it doesn't matter how fast my machine is, it can't compete with the network build our infra-team makes. So sure, an M3 might make my build 30% faster than my M1 build, but the network build is 15x faster. Is it possible instead of giving the developers M3s they should have inves…
It's like telling an indie hacker to adopt a complicated kubernetes setup for his app.
Earlier quoted context omitted.
That assumes you only use one laptop. I have a couple 2015 Macs that are very useful for browser tasks. They’re not paperweights and I use them daily.
I have a rack in my basement with a combined 96 cores and 192gb of ram (proxmox cluster), and a 13900k/64gb desktop workstation for most dev work. I usually will offload workloads to those before leveraging one of these old laptops that is usually dead battery. If I need something for "browser tasks" (I am interpreting this as cross-browser testing?) I have dedicated VMs for that. For just browsing the web, my M2 is…
Have a NAS to sync data files as needed.
As a scientist, I'm interested how computer programmers work with data. * They drew beautiful graphs! * They used chatgpt to automate their analysis super-fast! * ChatGPT punched out a reasonably sensible t test! But: * They had variation across memory and chip type, but they never thought of using a linear regression. * They drew histograms, which are hard to compare. They could have supplemented them with simple me…
Most of the time they deal with data the way their tools generally present data, which correlate closely to most analytics, perf analysis and observability software suites.
Expecting the average software eng to know what a CDF is the same as expecting them to know 3d graphics basics like quaternions and writing shaders.
Since RAM was a major metric, there should have been more focus on IO Wait to catch cases where OSX was being hindered by swapping to disk. (Yes, the drives are fast but you don’t know until you measure)
This. I’ve routinely got a 10-15GB page file on an M2 pro and need to justify bumping the memory up a notch or two. I’m consistently in the yellow memory and in the red while building. How can I tell how much I would benefit from a memory bump?
I am ex-core contributor Chromium and Node.js and current core contributor to gRPC Core/C++. I am never bothered with build times. There is "interactive build" (incremental builds I use to rerun related unit tests as I work on code) and non-interactive build (one I launch and go get coffee/read email). I have never seen hardware refresh toggle non-interactive into interactive. My personal hardware (that I use now and…
Cinebench and Geekbench tell a different story. M2 is a small upgrade over M1 while M3 is a bigger upgrade over M2.
Earlier quoted context omitted.
Ahh right, I'd seen a few comments about the memory bandwidth when it was posted on LinkedIn, specifically that the M2 was much more powerful. This makes a load of sense, thanks for explaining.
I've been considering buying a Mac specifically for LLMs, and I've come across a lot of info/misinfo on the topic of bandwidth. I see you are talking about M2 bandwidth issues that you read about on linkedin, so I wanted to expand upon that in case there is any confusion on your part or someone else who is following this comment chain. M2 Ultra at 800 GB/s is for the mac studio only. So it's not quite apples to apple…
I have not seen the equivalent comparisons with M[2-3] Max.