7 ms·
IBM scientists demonstrate 10x faster large-scale machine learning using GPUs
- web007 9y ago> We can see that the scheme that uses sequential batching actually performs worse than the CPU alone, whereas the new approach using DuHL achieves a 10× speed-up over the CPU. I had to get down to the graph to realize they're talking about SVM, not deep learning. This could be pretty cool. Training a SVM has usually been "load ALL the data and go", and sequential implementations are almost non-existent. Even if this was 1x or 0.5x speed and didn't require the entire dataset at once it's a big win.
- jamesblonde 9y agoYes, i felt cheated when i read it was about training 1/10th of ImageNet on a SVM. I guess IBM are desparate not to be left behind in the race for distributed deep learning platforms.
- pdeburen 9y agoYeah they get you really hyped up first and drop the bomb at the end. Still quite impressive speedup, would be better though to show it on a benchmark where SVM's are used in practice
- carlmr 9y agoLooking at the normal stuff coming out of IBM they're not associated with good software in my mind. So the more outrageous their claim, the less I believe them. They need to earn a reputation first.
- hueving 9y agoThat's like judging all of Google based on the quality of one product. With 10x as many employees as Google and a very loose organization, expecting any kind of reputation is folly. A product from an IBM consultant is about as related to a product from IBM Watson as is a product from Microsoft being related to a product from Apple.
- kayoone 9y agoIBM has 400k employees and god knows how many subsidiaries and divisions, do you really think you can paint them all with one brush because of some negative experience you had with one of their products ?
- carlmr 9y agoNot one, we have multiple IBM software products in my company, and they're all consistently the most terrible software you can imagine making. Sure they might have some divisions that do better, but I have yet to see them.
- samfriedman 9y agoTo be honest, I'd readily cheer any groups working on traditional machine learning advancements despite all the current hype for neural methods.
- madenine 9y agoI'll second that. For all the attention DL/ANNs get... there's still a lot of legwork going on out there using linear models, basic trees, etc. IIRC this years kaggle survey ranked Logistic Regression as the #1 most used model by a long shot.
- make3 9y agoneural networks are stacked logistic regressions. a lot of the deep learning research benefits logistic regression
- make3 9y agoyou say that as if it's empty hype. but it's not: deep learning works, and works much better by any reasonable metric than SVMs in most problems that require high to very high model capacity.
- nlperguiy 9y agoI know two projects (fairly simple implementation) of sequential SVM. I believe vowpal wabbit can also do max-margin optimization. http://leon.bottou.org/projects/lasvm http://leon.bottou.org/projects/lasvm http://leon.bottou.org/projects/sgd http://leon.bottou.org/projects/sgd
- Barrin92 9y ago>I had to get down to the graph to realize they're talking about SVM, not deep learning. there's still a ton of usage for classical learning algorithms. I'd be a very happy camper if we could speed SVMs up by a magnitude
- panosv 9y agoHow about if you did the same on 8 or 16 core CPU that can have much more than 16 GB of memory and is not as expensive to move data around its own memory?
- bitL 9y agoRoughly 1000x slower? GPUs nowadays have 5000+ "cores" inside.
- panosv 9y agoThat's the point. On the GPU side they use all the 5000+ cores to parallelize the algorithm (they use the hardware to its full potential). On the CPU side they use just one core (at least there is no mention around the cores used on the CPU). It's like saying a Camry beat a Ferrari in maximum speed, but you don't mention that the Ferrari was only in the first gear for that specific race.
- visarga 9y ago> they use the hardware to its full potential If only! In fact it's a struggle to utilize a GPU to its full potential because the communication bottleneck makes it infeasible. Compute is fast but data can't get there fast enough. The authors of this paper were saying the same thing in the promo video, in fact, they were working on making GPU's more efficient. Why would they do that if GPU's are using their "full potential" already?
- dragontamer 9y ago> Roughly 1000x slower? Not really. A modern Coffee Lake i7 has several distinct advantages over GPUs. (AMD Ryzen also has similar advantages, but I'm gonna focus on Coffee Lake) 1. AVX2 (256-bit SIMD), for 32-bit ints / floats that's 8 operations per cycle. AVX512 exists (16 operations per cycle) but it its only on Server architectures. Also, AVX512 has... issues... with the superscaling point#2 below. So I'm assuming AVX2 / 256-bit SIMD. 2. Superscalar execution: Every Skylake i7 (and Coffee Lake by extension) has THREE AVX ports (Port0, Port1, and Port5). We're now up to 24-operations per cycle in fully optimized code... although Skylake AVX2 can only do 16 Fused-multiply-adds at a time per core. 3. Intel machines run at 4GHz or so, maybe 3GHz for some of the really high core-count models. GPUs only run at 1.6GHz or so. This effectively gives a 2x to 2.5x multiplier. So realistically, an Intel Coffee Lake core at full speed is roughly equivalent to 32 GPU "cores". (8x from AVX2 SIMD, x2 or x3 from Superscalar, and x2 from clock speed). If we compare like-with-like, a $1000 Nvidia Titan X (Pascal) has 3584 cores. While a $1000 Intel i9-7900 Skylake has 10 CPU cores (each of which can perform as well as 32-NVidia cores in Fused MultiplyAdd FLOPs). i9-7900 Skylake is maybe 10x slower than an Nvidia Titan X when both are pushed to their limits. At least, on paper. And remember: CPUs can "act" like a GPU by using SIMD instructions such as AVX2. GPUs cannot act like a CPU with regards to latency-bound tasks. So the CPU / GPU split is way closer than what most people would expect. ------------- A major advantage GPUs have is their "Shared" memory (in CUDA) or "LDS" memory (in OpenCL). CPUs have a rough equivalent in L1 Cache, but GPUs also have L1 cache to work with. Based on what I've seen, GPU "cores" can all access Shared / LDS memory every clock (if optimized perfectly: perfectly coalesced accesses across memory-channels and whatever. Not easy to do, but its possible). But Intel Cores can only do ~2 accesses per clock to their L1 cache. GPUs can execute atomic operations on the Shared / LDS memory extremely efficiently. So coordination and synchronization of "threads", as well as memory-movements to-and-from this shared region is significantly faster than anything the CPU can hope to accomplish. A second major advantage is that GPUs often use GDDR5 or GDDR5x (or even HBM), which is superior main-memory. The Titan X has 480 GB/s (that's "big" B, bytes) of main memory bandwidth. A quad-channel i9-7900 Skylake will only get ~82 GB/second when equipped with 4x DDR4-3200MHz ram. GPUs have a memory-advantage that CPUs cannot hope to beat. And IMO, that's where their major practicality lies. The GPU architecture has a way harder memory model to program for, but its way more efficient to execute.
- WhitneyLand 9y agotldr: They made a caching algorithm. Article was touched by pr dept, but still has actually information. longer tldr: They did the same thing that has been done for thousands of years. Back then the hot area of research was how to stage advance food and resource caches along a route for long journeys. They came up with algorithms to optimize cache hits. In this case, the problem is GPUs can be fast for ML, but usually only have 16GB ram when dataset can be terabytes. Simple chunk processing would seem to solve the problem, but it’s turns out overhead of cpu/gpu transfers badly degraded performance. Their claim here is they can on the fly determine how important different samples are, and make sure samples that yield better results are in the chance more often than those with less importance.
- pdeburen 9y ago> Their claim here is they can on the fly determine how important different samples are, and make sure samples that yield better results are in the chance more often than those with less importance. Isn't that the exact same idea as in active learning?
- drb91 9y agoI don't think you need active learning to get results like this, just decent statistical analysis. There are parallels here with distributed query planning.
- alexasmyths 9y agoTo be fair - most innovation boils down to this kind of incremental stuff. 99% of 'tech' is an amalgamation of more basic ideas, not 'magic leap' kind of innovation. I mean, we all love the magic, but I think we're getting spoiled as of late with all the magic AI/Deep Learning stuff coming out.
- inputcoffee 9y agoI agree with your first statement to the point of disagreeing with your second. i.e. even the magic stuff is just incremental progress that people were not paying attention to. (Self driving cars have been wowing people since the 90s, object recognition just got incrementally better every year etc)
- calimac 9y agowhy not? every bitcoin miner was originally built on GPU's prior to them going to custom machines that ran on GPU type architecture.
- deleted 9y ago[deleted]
- LolWolf 9y agoThis is pretty fascinating! Though the concept seems to work only for convex problems (in particular, problems which have strong duality; this excludes NNs in almost their entirety, except 1 layer nets), but the application is nice and straightforward. I wonder if there is a similar lower-bound which can be constructed for non convex problems which retain enough properties for this method to be useful?
- dragontamer 9y agoI'd love to see more details. Ultimately, it seems like IBM has managed to make a generalized gather/scatter operation over large datasets in this particular task. Yes, this is an "old problem", but at the same time, its the kind of "Engineering advancement" that definitely deserves talk. Any engineer who cares about performance will want to know about memory optimization techniques. As CPUs (and GPUs! And Tensors, and FPGAs, and whatever other accelerators come out) get faster and faster, the memory-layout problem becomes more and more important. CPUs / GPUs / etc. etc. are all getting way faster than RAM, and RAM simply isn't keeping up anymore. A methodology to "properly" access memory sequentially has broad applicability at EVERY level of the CPU or GPU cache. From Main Memory to L3, L3 to L2, L2 to L1. The only place this "serialization" method won't apply is in register space. The "machine learning" buzzword is getting annoying IMO, but there's likely a very useful thing to talk about here. I for one am excited to see the full talk.
- web007 9y agoThey buried it, but their NIPS 2017 paper is linked in the article. https://arxiv.org/abs/1708.05357 https://arxiv.org/abs/1708.05357
- dragontamer 9y agoThanks. It does seem specific to machine learning / Tensors. But that's still cool. I'll have to sit down and grok the paper more carefully to fully understand what they're doing.
- yters 9y agoSVMs have better generalization possibilities than NNs, so this is neat.
- samnwa 9y agoHow do I use this to mine bitcoin? K thanks.