bnprks
Joined 4 years ago
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Comment · 9 months ago
Comment · 2 years ago
Amusingly, (to me at least) there's also an SSE instruction for non-reciprocal square roots but it's so much slower than reciprocal square root that calculating sqrt(x) as x * 1/sqrt(x) is faster assuming you can tolerate the somewhat reduced precision.
Comment · 2 years ago
Yeah, I think DRAM is almost certainly the future, just in terms of being able to afford the memory capacity to fit large models. Even Cerebras using a full wafer only gets up to 44 GB of SRAM on a chip (at a cost over $2M).
An interesting twist is that this DRAM might not need to be a central pool where bandwidth must be shared globally -- e.g. the Tensortorrent strategy seems to be aiming for using smaller chips that each have their own memory. Splitting up memory should yield very high aggregate bandwidth even with slower DRAM, which is great as long as they can figure out the cross-chip data flow to avoid networking bottlenecks
Comment · 2 years ago
Seems like the claims of the abstract for speed and energy-efficiency relative to an RTX 3090 are when the GPU is using a batch size of 1. I wonder if someone with more experience can comment on how much throughput gain is possible on a GPU by increasing batch size without severely harming latency (and what the power consumption change might be).
And from a hardware cost perspective the AWS f1.2xlarge instances they used are $1.65/hr on-demand, vs say $1.29/hr for an A100 from Lambda Labs. A very interesting line of thinking to use FPGAs, but I'm not sure if this is really describing a viable competitor to GPUs even for inference-only scenarios.
Comment · 2 years ago
The article also gives reason to be skeptical of the quoted "10 fatalities out of an estimated 3.65 million jumps in 2023". If we count 28 known fatalities at this one facility from 1983 to 2021, we get around 0.75 fatalities per year.
In other words, we would expect that 14 facilities of similar death counts to the one in the article would equal the total US fatalities for a year. The USPA dropzone locator [1] lists 142 facilities, so if we take everything at face value then this facility is ~10x worse than the average for USPA members.
> But I'd bet it's less than $200/jump worth of risk
In this case at least, it seems that this specific facility is higher risk than that. And with a lack of legally mandated reporting requirements, I'd say the onus is on a facility to prove safety once it's averaging a death every 1.3 years.
Comment · 2 years ago
Thanks for the link, and very good to know. I've always struggled to find component prices for Kioxia drives and higher-capacity RAM sticks so it's good to see I can finally look these up on serversupply when I'm curious.
Comment · 2 years ago
Are you sure you're comparing equivalent memory and storage specs? I needed to go into the customization menus in the Dell configurator to spec something equivalent, where prices started going up quite rapidly.
For example "3.2TB Enterprise NVMe Mixed Use AG Drive U.2 Gen4 with carrier" is $3,301.65 each, and you'd need 10 of those to match the Oxide storage spec -- already above the $30k total price you quoted. Similarly, "128GB LRDIMM, 3200MT/s, Quad Rank" was $3,384.79 each, and you'd need 8 of those to reach the 1TiB of memory per server Oxide provides.
With just the RAM and SSD cost quoted by Dell, I get to $60k per server (x16 = $960k), which isn't counting CPU, power, or networking.
I agree these costs are way way way higher than what I'd expect for consumer RAM or SSD, but I think if Oxide is charging in line with Dell they should be asking at least $1MM for that hardware. (At least compared to Dell's list prices -- I don't purchase enterprise hardware either so I don't know how much discounting is typical)
Edit: the specific Dell server model I was working off of for configuration was called "PowerEdge R6515 Rack Server", since it was one of the few I found that allowed selecting the exact same AMD EPYC CPU model that Oxide uses [1]
[1]: https://www.dell.com/en-us/shop/dell-poweredge-servers/power...
Comment · 2 years ago
The specifications page [1] gives a bit more context. I think minimum buy is about a half rack, which includes at least 16 64-core CPUs, 16 TiB of RAM, and 465.75 TiB of NVMe SSD storage. Playing around a bit with the Dell server configurator tool, it seems like that is going to come in a rough ballpark of $1MM as stated in a sibling comment.
Comment · 2 years ago
If the function chooses to overwrite the value of a variable binding, it doesn't matter how it is defined at the call site (so inner x wins in your example). In the tidyverse libraries, they often populate a lazy list variable (think python dictionary) that allows disambiguating in the case of name conflicts between the call site and programmatic bindings. But that's fully a library convention and not solved by the language.
Comment · 2 years ago
The good news is that most variables in R are immutable with copy-on-write semantics. Therefore, most of the time everything here will be side-effect-free and any weird editing of the variable bindings is confined to within the function. (The cases that would have side effects are very uncommonly used in my experience)
Comment · 2 years ago
One of the wildest R features I know of comes as a result of lazy argument evaluation combined with the ability to programmatically modify the set of variable bindings. This means that functions can define local variables that are usable by their arguments (i.e. `f(x+1)` can use a value of `x` that is provided from within `f` when evaluating `x+1`). This is used extensively in practice in the dplyr, ggplot, and other tidyverse libraries.
I think software engineers often get turned off by the weird idiosyncrasies of R, but there are surprisingly unique (arguably helpful) language features most people don't notice. Possibly because most of the learning material is data-science focused and so it doesn't emphasize the bonkers language features that R has.
Comment · 2 years ago
To my knowledge AlphaGo models never became meaningfully available to the public, but 8 years later the KataGo project has open source, superhuman Go AI models freely available and under ongoing development [1]. The open source projects that developed in the wake of AlphaGo and AlphaZero are a huge success story in my mind.
I haven't played Go in a while, but I'm kind of excited to try going back to use the KataGo-based analysis/training tools that exist now.
Comment · 2 years ago
I'm sorry about negative experiences and/or regrets other commenters might have about their vaccinations. Measuring the risk/reward profile of vaccines seems far from simple, particularly in cases like this where the large benefits (no cancer) and risks (autoimmune problems) may both be quite rare for any individual. It is too bad if the study didn't fully capture possible risks in this case, and hopefully follow-up studies and monitoring can help better describe the risk profile.
It's worth noting the benefits of HPV vaccination do seem to be quite real, though. In the US, >20% of the female population has a high-risk HPV infection [1], and cervical cancer runs at ~12k new cases and ~4k deaths a year [2]. A follow-up study found women vaccinated before age 17 had about 88% reduction in cervical cancer, with around 53% for women vaccinated at 17-30 years of age [3] (presumably later-vaccinated women had a high chance of already having an HPV infection so the vaccine wouldn't be useful).
I think potentially saving >3.5k lives and >10k cervical cancer cases annually in the US is a pretty good return if we can get widespread HPV vaccination, though of course we should also work hard to study and minimize vaccine side-effects. I'm similarly hopeful of news about EBV as a cause of multiple sclerosis [4], which is another situation where preventing a widespread infection might prevent rare but serious illnesses.
[1] https://www.cdc.gov/nchs/products/databriefs/db280.htm
[2] https://gis.cdc.gov/Cancer/USCS/#/Trends/1,2,73,1,3,value,23
[3] https://www.cancer.gov/news-events/cancer-currents-blog/2020...
[4] https://www.hsph.harvard.edu/news/press-releases/epstein-bar...
Comment · 2 years ago
Strong second for wishing they tried physically testing some model output. The importance of "model that makes outputs AlphaFold thinks look like Cas" is very different from "model that makes functional Cas variants".
For design tasks like in this paper, I think computational models have a big hill to climb in order to compete with physical high-throughput screening. Most of the time the goal is to get a small number of hits (<10) out of a pool of millions of candidates. At those levels, you need to work in the >99.9% precision regime to have any hope of finding significant hits after multiple-hypothesis correction. I don't think they showed anything near that accurate in the paper.
Maybe we'll get there eventually, but the high-throughput techniques in molecular biology are also getting better at the same time.
Comment · 2 years ago
Sadly even SSE vs. AVX is enough to often give different results, as SSE doesn't have support for fused multiply-add instructions which allow calculation of a*b + c with guaranteed correct rounding. Even though this should allow CPUs from 2013 and later to all use FMA, gcc/clang don't enable AVX by default for the x86-64 targets. And even if they did, results are only guaranteed identical if implementations have chosen the exact same polynomial approximation method and no compiler optimizations alter the instruction sequence.
Unfortunately, floating point results will probably continue to differ across platforms for the foreseeable future.
My best experience has been ignoring SQL and using (sparse) matrix formats for the genomic data itself, possibly combined with some small metadata tables that can fit easily in existing solutions (often even in memory). Sparse matrix formats like CSC/CSR can store numeric data at ~12 bytes per non-zero entry, so a single one of your servers should handle 10B data points in RAM and another 10x that comfortably on a local SSD. Maybe no need to pay the cost of going distributed?
Self plug: if you're in the single cell space, I wrote a paper on my project BPCells which has some storage format benchmarks up to a 60k column, 44M row RNA-seq matrix.