We believe in the power of science and a knowledge-based approach to life in general. So we’re proud to be a part of several community grid computing projects which work to solve open problems in medicine, environmental science, and mathematics. Our contributions We currently are, or previously have been, active in World Community Grid, Folding@Home, Rosetta@Home, Einstein@Home, GPUGrid, and Asteroids@Home. You can check out our detailed stats across all projects.
2021-11-04: More power With temps continuing to drop outside, I’ve bumped the PPT on the 3950Xs to 80W, so they’re now sitting right at 60C with average clocks around 2.7GHz. 2021-10-20: node01 GPU upgrade The GTX 1650 that I snagged arrived today and has been slotted into node01. That bumps its FAH EPPD from the range of 50-100k to 500k+. Also, while node05’s 750 Ti is soldiering on, it’s become apparent that it should be upgraded as well.
We want our hardware to keep doing good work after it’s cycled out of active use in our fleet. So we do rebuilds, turning extra hardware from upgrades into complete machines. These machines are then distributed to people who can give them a good home. CPU RAM GPU Storage New assignment Ryzen R5 1600 32G GTX 1650 S 500G M.2 Climatology; science communication Ryzen R3 2200G 16G — 500G M.
2020-12-18: Second Century Today marks 200 years of CPU time for World Community Grid, 340 calendar days after hitting the first century mark. Upgrades next year will let us do even more science in less time. 2020-08-25: Wrapping up Rosetta With WCG’s OPN project in full swing, we are finishing up our existing work for Rosetta@Home, and then detaching our nodes for now. Rosetta is very heavy, and it’s also extremely popular so it’ll be fine while we put those cycles toward WCG’s projects.
This is the development diary for Greenhouse 2, the second iteration of my homebrew micro-rack. 2020-05-01 – Introduction The original Greenhouse was a success, but several lessons were learned from it. Use of mITX boards minimized footprint, but drove up cost and reduced choice Over/under PSU design minimized horizontal footprint, but led to increased vertical spacing and more complex mounting Minimizing horizontal footprint wasn’t worth it Fully open design probably didn’t cool as effectively; pushing air where you want it isn’t as effective as forcing it to be pulled through where you need it Even moderately complex cutting and shaping of metal is a nightmare without proper tooling and space And so the next iteration will be changed in several fundamental ways.
I’ve been working on adding support for the ARM architecture to Homefarm for a bit now. This week I decided to try to push through, and get that work done and tested. Obviously you can’t test software designed to manage a farm of computers with just one machine, so I had to build myself a tiny ARM farm. Building the cube Here’s most of the raw materials: Four Raspberry Pi 4 Model Bs (4GB version), 4 heatsink sets, and 3 16GB SD cards (I already had one spare).
2019-11-03: 75 years; last update of the year Today we crossed 75 years of compute time for WCG. Two other events happened earlier this week: the Africa Rainfall Project kicked off, and we finished our 3900X upgrade. We’re excited about both, especially since finishing the upgrades let us build two more nodes out of spare parts. We now have 128 threads in-house. Here’s to next year! 2019-08-16: Top 1000; New hardware; Visiting friends Thanks to a visit from user Sheridon of Xstreme Systems Team, we broke into the top 1000 teams a few days earlier than expected.
The Firepear computing stack is currently all Ryzen. It’s simple cost-benefit analysis: I’m interested in crunching as much data as possible per unit time, at a reasonable cost (in both money and electricity). Right now, that’s Ryzen. I’m upgrading all my machines to the new R9 3900X CPU. This provides an opportunity to compare all three generations of the Ryzen family. 3900X vs 3950X I want to throw as many cores as I can at the problems that I volunteer compute time for, but I also have a finite budget.
After months of pondering what GPU to use to get my compute farm back into crunching GPGPU workunits, I was persuaded by a recent review to order a GTX 1650 and give it a go. Background and lead-up Years ago, I was reading stories about how Linux support on Steam had really improved. I decided to build a Linux box capable of running modern-ish games. This was in 2015, a time when AMD GPU support on Linux was abominable, so my choice for a video card boiled down to “which Nvidia card?
This is the development diary for Greenhouse, my attempt to build a DIY compute farm in a box. In other words, a multi-machine chassis. 2018-11-01 — Introduction and Plan The core idea is to assemble individual compute nodes on trays, in the most compact way feasible, and then to stack nodes atop each other in an enclosure. The components of each node will be: A tray made from some thin, rigid, wood-based board.