cuno

This stuff dates way, way before 2004.

For non-power of two, just checked our own very old circular byte buffer library code and using the notation from this article, it is:

  entriesAllocated() { return ((wrPtr-rdPtr+2*bufSize) % (2*bufSize)); }
  remainingSpace() { return bufSize - entriesAllocated(); }
  isEmpty() { return (entriesAllocated()==0); }
  isFull() { return (entriesAllocated()==bufSize); }
  incWr(int n) { wrPtr = (wrPtr+n) % (2*bufSize); }
  incRd(int n) { rdPtr = (rdPtr+n) % (2*bufSize); }

The 2*bufSize gives you an extra bit (beyond representing bufSize) that lets you disambiguate empty vs full. And if it is a constant power of two (e.g. via C++ template), then you can see how this just compiles into a bitmask instead, like the author's version. You read and write the buffer at (rdPtr%bufSize) and (wrPtr%bufSize) respectively.

So after transforming multispectral satellite data into a 128-dimensional embedding vector you can play "Where's Wally" to pinpoint blackberry bushes? I hope they tasted good! I'm guessing you can pretty much pinpoint any other kind of thing as well then?

We ended up overriding and replacing with our own thread-safe version years ago when we also hit this.

Good to see you too! Lets catchup sometime - tried to connect with you separately but no luck.

Founder of cunoFS here, brilliant to see lots of activity in this space, and congrats on the launch! As you'll know, there's a whole galaxy of design decisions when building file storage, and as a storage geek it's fun to see what different choices people make!

I see you've made some similar decisions to what we did for similar reasons I think - making sure files are stored 1:1 exactly as an object without some proprietary backend scrambling, offering strong consistency and POSIX semantics on the file storage, with eventual consistency between S3 and POSIX interfaces, and targeting high performance. Looks like we differ on the managed service vs traditional download and install model, and the client-first vs server-first approach (though some of our users also run cunoFS on an NFS/SMB gateway server), and caching is a paid feature for us versus an included feature for yours.

Look forward to meeting and seeing you at storage conferences!

Dynasaur intercepts syscalls in Linux entirely in userspace, superfast and across static, semi-static and dynamic binaries. It even works inside containers and doesn't need admin or any special privileges like PTRACE, or need a VM like GVisor. We built it for our cunoFS virtual filesystem and we want to know if others are interested in buliding on top of it for other use cases?

Yes Bali was the next gen architecture and incredibly scalable. It consisted of many different chips connected together in a network that could scale. The R chip was so big existing tools couldn't handle it and ppl were writing their own tools. As a result it was very expensive to tape out so many hefty chips and I think that's why when it came time, and with a financial crisis, upper management pulled the plug.

Yes there were separate teams working on the lower-end graphics.

Nintendo wasn't loyal to the company it was loyal to the team, so when they just decided to leave and form ArtX they took the customer with them... SGI was happy with the Nintendo contract. They earned $1 in additional royalties for every single N64 cartridge sold worldwide. Losing the team was a big blow.

I worked at SGI on the next generation (code named Bali) in 1998 (whole year as an intern) and 1999 (part time while finishing my degree, flying back and forth from Australia). Bali was revolutionary. The goal was realtime Renderman and it really would. I had an absolute blast. I ended up designing the highspeed data paths (shader operations) for world's first floating point frame buffer (FP16 though we called it S10E5) with the logic on embedded DRAM for maximum floating point throughput. It was light years ahead of its time. But the plug got pulled just as we were taping out. Most of the team ended up at Nvidia or ArtX/ATI. The GPU industry was a small world of engineers back then. We'd have house parties with GPU engineers across all the company names you'd expect, and with beer flowing sometimes maybe a few secrets could eh spill. We had an immersive room to give visual demos and Stephen Hawking came in once pitching for a discount.

For team building, we launched potato canons into NASA Moffet field, blew up or melted Sun machines for fun with thermite and explosives. Lots of amazing people and fond memories for a kid getting started.

Sorry for the late response - I didn't see your comment until now.

Our aim is to unleash all the potential that S3/Object has to offer for file system workloads. Yes, the scale of AWS S3 helps, as does erasure coding (which enhances flexibility for better load balancing of reads).

Is it suitable for every possible workload? No, which is why we have a mode called cunoFS Fusion where we let people combine a regular high-performance filesystem for IOPS, and Object for throughput, with data automatically migrated between the two according to workload behaviour. What we find is that most data/workloads need high throughput rather than high IOPS, and this tends to be the bulk of data. So rather than paying for PBs of ultra-high IOPS storage, they only need to pay for TBs of it instead. Your particular workload might well need high IOPS, but a great many workloads do not. We do have organisations doing large scale workloads on time-series (market) data using cunoFS with S3 for performance reasons.

The problem with these approaches is that the data is scrambled on the backend, so you can't access the files directly from S3 anymore. Instead you need an S3 gateway to convert from scrambled S3 to unscrambled S3. They rely on a separate database to reassemble the pieces back together again.

IOPS is a really lazy benchmark that we believe can greatly diverge from most real life workloads, except for truly random I/O in applications such as databases. For example, in Machine Learning, training usually consists of taking large datasets (sometimes many PBs in scale), randomly shuffling them each Epoch, and feeding them into the engine as fast as possible. Because of this, we see storage vendors for ML workloads concentrate on IOPS numbers. The GPUs however only really care about throughput. Indeed, we find a great many applications only really care about the throughput, and IOPS is only relevant if it helps to accomplish that throughput. For ML, we realised that the shuffling isn't actually random - there's no real reason for it to be random versus pseudo-random. And if its pseudo-random then it is predictable, and if its predictable then we can exploit that to great effect - yielding a 60x boost in throughput on S3, beating out a bunch of other solutions. S3 is not going to do great for truly random I/O, however, we find that most scientific, media and finance workloads are actually deterministic or semi-deterministic, and this is where cunoFS, by peering inside each process, can better predict intra-file and inter-file access patterns, so that we can hide the latencies present in S3. At the end of the day, the right benchmark is the one that reflects real world usage of applications, but that's a lot of effort to document one by one.

I agree that things like dedupe and compression can affect things, so in our large file benchmarks each file is actually random. The small file benchmarks aren't affected by "write bigger blocks" because there's nothing bigger than the file itself. Yes, data consistency can be an issue, and we've had to do all sorts of things to ensure POSIX consistency guarantees beyond what S3 (or compatible) can provide. These come with restrictions (such as on concurrent writes to the same file on multiple nodes), but so does NFS. In practice, we introduced a cunoFS Fusion mode that relies on a traditional high-IOPS filesystem for such workloads and consistency (automatically migrating data to that tier), and high throughput object for other workloads that don't need it.

It depends on if you want to expose filesystem semantics or metadata to applications using it. For example random access writes are done by ffmpeg, which is a workhorse of the media industry, but most things can't handle that or are too slow. We had to build our own solution cunoFS to make it work properly at high speeds.

For example, you can do separate parallel ListObjectV2 for files starting a-f and g-k, etc.. covering the whole key space. You can parallelize recursively based on what is found in the first 1000 entries so that it matches the statistics of the keys. Yes there may be pathological cases, but in practice we find this works very well.