7 ms·
Fire-Flyer File System (3FS)
- do_not_redeem 2y agoCan someone convince me this isn't NIH syndrome? Why would you use this instead of SeaweedFS, Ceph, or MinIO?
- startupsfail 2y agoIt’s not. When you are a high frequency trader and you’ve mastered RDMA, everything around you looks slow. You are thinking in terms of 20 nanoseconds intervals, while everyone around still thinks that serving a query under a millisecond is fast.
- rfoo 2y agoHuh? What kind of RDMA has a completion latency of 20 nanoseconds? It's more like 5 microseconds. I agree that a lot of "modern" storage stack is way too slow though, tried to find a replication-first object storage for crazy-fast random read in small number of objects last year and found none.
- tucnak 2y agoCompletion latency is one thing, bandwidth would be another. There's apparently a whole world of Alveo SmartNIC's and related FPGA platforms, and it can totally get in nanosecond range for whatever nails that may fit the compute-in-network hammer, even if bound by latency of the consuming system / RDMA interface. Also: https://github.com/corundum/corundum https://github.com/corundum/corundum is really popular with the Chinese!
- startupsfail 2y agoI was talking about, thinking in terms of 20 nanoseconds intervals, rather than completing a request in 20 nanoseconds. To get 1 microsecond wire-to-wire latency you do need to count your nanoseconds. Why this number - this is because it’s roughly the time it takes to read 64 bytes from L3 cache. And NICs tend to be able to push data into L3 (or equivalents). Current state of the art - look up nanoPU, from Stanford. Wire-to-wire under 100ns is not impossible, but this would normally assume pre-cooked packet, selected from a number of packets (which is not an unusual scenario in HFT).
- rfoo 2y agoAh, makes sense. Sadly RDMA isn't that fast for now, or at least commercial RNICs/switches don't :( Once you left your host in data center network, everything counts in microseconds.
- ein0p 2y agoSeems like Ceph is considerably lower in throughput: https://ceph.io/en/news/blog/2024/ceph-a-journey-to-1tibps/ https://ceph.io/en/news/blog/2024/ceph-a-journey-to-1tibps/ A serious concern when saving hundreds of terabytes of weights and optimizer states every now and again, or loading large precomputed prefix KV-caches. Minio seems to be slower still. IDK about SeaweedFS - they don't mention performance in their selling points at all.
- do_not_redeem 2y agoIt's quite funny that I got two opposite answers right away: you say it's to improve throughput, and sibling says it's to improve latency, and as we know throughput and latency trade off against each other. I'm inclined to agree it's more likely they're prioritizing throughput, since their readme charts throughput but not latency. But OTOH, the project looks like it requires RDMA. I wonder if the authors have written about their motivations and the tradeoffs they made, so we don't have to speculate. EDIT: Their blog post answered all my questions and more. https://www.high-flyer.cn/blog/3fs/ https://www.high-flyer.cn/blog/3fs/
- ein0p 2y agoBecause the two are interconnected and aren't in conflict with each other. You not only want high throughput - that by itself would be quite limiting. You want it along with low latency as well, or else it's very easy to end up in the situation where your throughput is effectively zero if the access pattern is "bad".
- ibotty 2y agoLook at the hardware first: The hardware in the Ceph test is only capable of max 1.7TiB/s traffic (optimally without any overhead whatsoever). I also assume that the batch size (block size) is different enough that this alone would make a big difference.
- mgerdts 2y ago> The final aggregate read throughput reached approximately 6.6 TiB/s with background traffic from training jobs. The Ceph team has been working on Crimson for years to get past performance bottlenecks inherent to the HDD-based design. I’m having troubles finding any ceph benchmark results that show any close to 100 GB/s.
- deleted 2y ago[deleted]
- pat2man 2y agoSeems easy to find: https://ceph.io/en/news/blog/2024/ceph-a-journey-to-1tibps/ https://ceph.io/en/news/blog/2024/ceph-a-journey-to-1tibps/
- menaerus 2y ago3FS: 180 nodes, 2x200Gbps InfiniBand and 16x 14TiB NVMe SSDs per node, ~500 clients, 6.6 TiB/s of read throughput with training jobs workload Ceph: 68 nodes, 2x100Gbps Mellanox and 10x 14TiB NVMe SSDs per node, 504 clients, 1TiB/s of FIO random read workload
- ibotty 2y agoThe comparison is a little pears to apple. Similar nutritions but different enough to not draw conclusions. The hardware in the Ceph test is only capable of max 1.7TiB/s traffic (optimally without any overhead whatsoever). I also assume that the batch size (block size) is different enough that this alone would make a big difference.
- menaerus 2y agoEven if we take different hardware into account we can readjust for measured vs theoretical throughput. Ceph cluster achieves 1 TiB/s / 1.7 TiB/s = 0.58% of theoretical throughput. 3FS cluster achieves 6.6 TiB/s / 9 TiB/s = 0.73% of theoretical throughput.
- cttet 2y agoIf NIH syndrome boosts morale of the team, it should be helpful on overall team progress though.
- jpgvm 2y agoNone of those are close to fast enough. The only competitors in the parallel FS space that are useful for this are Lustre and Weka. Otherwise if you don't need a single namespace a bunch of fat AF NFSv4 servers w/NFS over RDMA will also get you to 6TiB/s. The "surefire" way though is still Lustre, it's the big daddy of distributed parallel filesystems still but it's an absolute beast to setup and operate.
- doomleika 2y agoSoftware tech in China is a different landscape. It's really common to reinvent the wheel in China. Almost every big name (Bytedance, Meituan, etc) have their own of everything with both office political and in house need reasons. The thing is those stuff are so prevalent those in house tech have reach the point they are competitive. This doubles for quant firm like DeepSeek.
- whalesalad 2y agoSometimes you must succumb to NIH. How do you think all those tools you mentioned got produced?
- brcmthrowaway 2y agoWhat does Anthropic use?
- pepsi-not-coke 2y agoI love it. AWS EFS costs too much. The open source solutions are clunky. I'm hoping DS applied their ingenuity to this one, too. Can't wait to trial it.
- tetron 2y agoWas curious how they get such performance with a FUSE based design. It seems that they sort of cheat, FUSE is used to manage metadata but to get high performance you have to link in the C++ client library and do all your reads and writes through that. So it isn't general purpose, you have to modify your application to take advantage of it. Still, that's a clever trick, and makes me wonder if there's a LD_PRELOAD strategy that could generalize.
- grohan 2y agoThey appear to have Python bindings which seems reasonable from an API / usability perspective? https://github.com/deepseek-ai/smallpond https://github.com/deepseek-ai/smallpond In terms of fast FUSE - also my first question, appears to be`io_uring` + FUSE :) https://github.com/deepseek-ai/3FS/blob/main/src/lib/api/UsrbIo.md https://github.com/deepseek-ai/3FS/blob/main/src/lib/api/Usr...
- amelius 2y agoWhy is FUSE that much slower than providing your own read/write functions? I get that it has to go through the kernel, but the operations are on entire blocks and network should be the bottleneck by far (and disk/main memory should be a bottleneck if the data is local).
- vlovich123 2y agoYou have to bounce through the kernel back out to use space. The number of syscalls is quite high. In many cases this is mitigated somewhat by the page cache making reads cheaper, but that’s explicitly an anti design here. I believe there’s work to minimize this using io_uring so that you can talk to the fuse driver without the kernel being in the middle, but that work isn’t ready last time I checked. For what it’s worth at Palm we had a similar problem because our applications were stored compressed but exposed through fuse uncompressed, instead of O_DIRECT I just did an fadvise to dump the cache after a read. Not as high throughput but the least risky change to get the same effect.
- 2y ago
- ammo1662 2y agoFor those who are interested, the design was originally published here: (Chinese) https://www.high-flyer.cn/blog/3fs/ https://www.high-flyer.cn/blog/3fs/ This file system has been developed and utilized by them for several years . Compared to the traditional file systems, it is more focused on model training that contains a lot of random reads. Read cache and prefetching are useless in this case. Therefore, they designed the file system without those features to improve the performance. I google translated some key parts here: 3FS is a special file system because it is almost only used in the scenario of batch reading sample data in the computing node during AI training, and accelerates model training through high-speed computing and storage interaction. This is a large-scale random reading task, and the read data will not be used again in a short time, so we cannot use the most important tool "read cache" to optimize file reading, and even advance reading is useless. Therefore, the implementation of 3FS is also quite different from other file systems. Specifically, as shown in the figure above, 3FS uses the Linux-based AIO and io_uring interfaces to complete sample reading, because in the 3FS scenario, File Cache has no effect at all, but will consume system memory in a way that is difficult for users to control, affecting the operation of subsequent tasks, so we turned off File Cache and only used Direct I/O mode to read data. But it should be noted that when reading in this way, the buffer pointer, offset and length all need to be aligned. If the user is allowed to do this alignment, additional memory copies will be generated, so we have done the alignment inside the file system, which not only optimizes performance but also facilitates users.
- rvba 2y agoWhy is that a random read? Also is it truely random, or from seed? But if prng then they could cache right?
- kilburn 2y agoRandom is prng. They still cannot cache though because they do many reading "passes" through the same data. If build a cache that gets hits on the first pass, then it won't work for the second and later passes.
- vlovich123 2y ago
- budududuroiu 2y agoDoes anyone know if there’s a benefit to porting this to an orchestrator like K8s, maybe overkill for training but the KVCache might be useful when having multiple replicas for inference?
- jeffbee 2y agoInteresting that their GraySort result is CPU bound while they are using 3x more CPUs than the record holder from ten years ago.
- bee_rider 2y agoThey sure are productive. What are we going to see tomorrow? DeepSeek OS or something?
- deleted 2y ago[deleted]
- logicallee 2y ago>They sure are productive. I have a theory as to why...
- tuyguntn 2y agoenlighten us
- logicallee 2y agomy theory is that their own DeepSeek writes the code for them, so they are highly productive.
- menaerus 2y agoThat would be terrifying in itself if true because for this type of work you really need the best of the best. But I doubt this is the case here. LLMs as we know them today are not quite yet there for this type of work.
- genewitch 2y agoDo you, though? Need the best of the best?
- bee_rider 2y agoAs someone who did some simulation focused engineering grad school stuff; there is a tendency for some of the best to go become quants. Does the field need it? I don’t know. But for whatever reason the draw of the “print money using math tricks” seems to attract some hardcore folks, haha. It is really frustrating to see good engineers go to play trading games. We should study how exactly it is China managed to unlock this capacity.
- thohj4234234324 2y agoThis is very humbling. OpenAI et. al kind of have also been very deep down the systems rabbit hole (eg. Triton), but I can't think of anyone else (outside of Google/Facebook) who pay this amount to attention to things. Great work; hope Deepseek does even more awesome things going forward.
- richardw 2y agoI’ve assumed that it’s partly because the company has done a lot of HFT, which is very focused on performance. But I’m not an expert in either.
- WiSaGaN 2y agoIndeed, the blog mentioned in the other comment showed part of 3FS code was completed at least since 2019, when this was still a project of the quant funds. In HFT, you tend to dogfood a lot of the things to achieve low latency, high performance, sometimes just because HFT system just need to do one specific thing, and those off the shelf stuff usually cater for a lot wider scenarios where HFT doesn't really care about. Here you see similar case which they focus specifically on loading large amount of data during training, and implement that to the extreme.
- deleted 2y ago[deleted]
- jauntywundrkind 2y agoMan, 6.6TB/s across 180 nodes is 300Gbps/node, or 37.5GBps. That's with 14 unnamed SSD per node. I wonder how this would scale to higher end SSD,dealing from PCIe 4 to PCIe 5 or PCIe 6... Particularly whether one could scale down!
- WithinReason 2y agoWhy is this even necessary? Can you just shard your training set to the training nodes ahead of time instead?
- wenyuanyu 2y agoNo, besides accessing training data, there is also logging and checkpointing... When you run k8s over it, and there are multiple training jobs... isolated local storage is a nightmare...
- yalogin 2y agoIt’s not clear to me where and how the current popular systems fall short. Do they talk about I anywhere? Also, what specifically is the data access patterns for training and inference that are different from traditional use cases?
- jpgvm 2y agoWell current popular systems are pretty much limited to Lustre and the new kid Weka, mostly Lustre though tbh. You can try to use "standard" options like MinIO/Ceph(RADOS)/SeaweedFS but you will very quickly learn those systems aren't remotely fast enough for these usecases. AI training is what this is used for, not inference (which has absolutely no need for any filesystem at all). What makes the workload somewhat special is that it's entirely random read and not cacheable at all as most reads are one and done. Would Lustre be perfectly fine at 6TiB/s? Yes. Is it a huge pain in the ass to operate and make remotely highly available? Also yes. If this thing is capable of the throughput but easier to operate and generally more modern and less baroque it's probably an improvement. TLDR is Lustre is fast but that is literally it's only redeeming quality. I have lost far too many hours of my life to the Lustre gods.
- cyanf 2y agoThey’re using the FS for caching the KV caches of past requests. It’s why they’re able to charge so little on prompt cache hit.
- jpgvm 2y agoAhh I missed that. Yes prefix caching and RAG are 2 cases were you will want something like this during inference time.
- rfoo 2y ago> What makes the workload somewhat special is I'll add that latency also doesn't matter that much. You are doing batched data loading for batch n+1 on CPU when GPUs are churning batch n-1 and copying batch n from host memory at the same time. So as long as your "load next batch" doesn't run for like >1s it would be fine. But one single "load next batch" on one worker means thousands (if not more) random read.
- pella 2y agorelated research paper (english - html ) - https://arxiv.org/html/2408.14158v2 https://arxiv.org/html/2408.14158v2 arXiv:2408.14158v2 [cs.DC] 31 Aug 2024 "Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning" Abstract: "The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly inflated High Performance Computing (HPC) construction costs. To address these challenges, we introduce the Fire-Flyer AI-HPC architecture, a synergistic hardware-software co-design framework and its best practices. For DL training, we deployed the Fire-Flyer 2 with 10,000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%. We specifically engineered HFReduce to accelerate allreduce communication and implemented numerous measures to keep our Computation-Storage Integrated Network congestion-free. Through our software stack, including HaiScale, 3FS, and HAI-Platform, we achieved substantial scalability by overlapping computation and communication. Our system-oriented experience from DL training provides valuable insights to drive future advancements in AI-HPC."
- codingwagie 2y agoI think the difference between deepseek and OpenAI/Anthropic is one of the difference between practitioners and academics. Ofcourse there is world class talent at OpenAI. But there are also alot of "I went to Harvard and want to work in AI", and those types of people just simply dont have the technical exposure to even think of building something like this.
- mustpax 2y agoSomeone should write a blog post about the prestige/effectiveness negative feedback loop. This is also the Achilles heel of top tier SV VCs including YC.
- deleted 2y ago[deleted]
- dvaun 2y agoCan you expand on this?
- djtango 2y agoYou mean this one? https://news.ycombinator.com/item?id=9125816 https://news.ycombinator.com/item?id=9125816
- bugglebeetle 2y agoThe problem isn’t the prestige it’s that prestigious institutions in America don’t produce high-quality talent. They’re instead mostly corrupt credentialing mills for the rich and well-connected. From what I understand, DeepSeek also only hires from the best universities in China, but “best” actually means something relative to how difficult entrance to those organizations is to achieve and their coursework.
- InkCanon 2y agoI read this too but there was no source on this. The founder Liang Wenfeng himself comes from Zhejiang university. Its admissions rate is 20%, which is much higher than traditional US "elite" schools. Wenfeng has said this about hiring though: "If you are pursuing short-term goals, it is right to find people with ready experience. But if you look at the long-term, experience is not that important. Basic skills, creativity, and passion are much more important.”
- whalesalad 2y agoThe throughput on those charts is pretty wild - multiple terabytes per second.
- hintymad 2y agoA distributed file system is honed as one of the trickiest software to write, and we are usually advised not to write a file system from scratch (even on top of FUSE), let alone a highly optimized one. When a silicon value company is having the 100th meeting to align god-knows-what, a team of fewer than 60 already came up with a production-grade highly efficient parallel file system. Have we in the valley companies lost touch?
- htrp 2y ago> team of fewer than 10 the highflyer team are pretty well resourced.... think they have more than 10 people
- hintymad 2y agoThanks! Updated to 60 per their author list in their paper.
- ssivark 2y agoWhy do you assume everyone in the company (including folks working on infra) are authors on the paper? That’s possible, of course, but isn’t it unlikely?
- hintymad 2y agoThat's the only information I have. That said, High-Flyer had about 160 people, total, in 2021. Given that F3 was in production in 2019, 60 people is a generous estimation.
- ycui1986 2y agoyes
- rvz 2y agoOnce again, DeepSeek continues with another home run. Can't wait to see what they release next. DeepSeek should be studied carefully.