3 ms·
TPUs are only one part of this eye-opening presentation. Skip to page 28, where Jeff starts talking about: * Using reinforcement learning so the computer can f
by cs702 9y ago
TPUs are only one part of this eye-opening presentation. Skip to page 28, where Jeff starts talking about:
* Using reinforcement learning so the computer can figure out how to parallelize code and models on its own. In experiments, the machine beats human-designed parallelization.
* Replacing B-tree indices, hash maps, and Bloom filters with data-driven indices learned by deep learning models. In experiments, the learned indices outperform the usual stalwarts by a large margin in both computing cost and performance, and are auto-tuning.
* Using reinforcement learning to manage datacenter power. Machine intelligence outperforms human-designed energy-management policies.
* Using machine intelligence to replace user-tunable performance options in all software systems, eliminating the need to tweak them with command line parameters like --num-threads=16, --max-memory-use=104876, etc. Machine intelligence outperforms hand-tuning.
* Using machine intelligence for all tasks currently managed with heuristics. For example, in compilers: instruction scheduling, register allocation, loop
nest parallelization strategies, etc.; in networking: TCP window size decisions, backoff for retransmits, data compression, etc.; in operating systems: process scheduling, buffer cache insertion/replacement, file system prefetching, etc.; in job scheduling systems: which tasks/VMs to co-locate on same machine, which tasks to pre-empt, etc.; in ASIC design: physical circuit layout, test case selection, etc. Machine intelligence outperforms human heuristics.
IN SHORT: machine intelligence (today, that means deep learning and reinforcement learning) is going to penetrate and ultimately control EVERY layer of the software stack, replacing human engineering with auto-tuning, self-improving, better-performing code.
Eye-opening.
- est 9y agoI remember there was a joke that in google's code base, there are more Bayesian cases than if...else...
- pramodliv1 9y agoIt was a quote in Joel Spolsky's blog. > A very senior Microsoft developer who moved to Google told me that Google works and thinks at a higher level of abstraction than Microsoft. “Google uses Bayesian filtering the way Microsoft uses the if statement,” he said https://www.joelonsoftware.com/2005/10/17/news-121/ https://www.joelonsoftware.com/2005/10/17/news-121/
- jamesblonde 9y agoGood summary. Some systems groups are already going in this direction. PeletonDB is trying to use DL to build a self-tuning DB https://github.com/cmu-db/peloton https://github.com/cmu-db/peloton We have been trying to self-tune resource management decisions in Hadoop YARN using deep learning.
- jacksmith21006 9y agoGreat summary. Thanks and agree this is a big deal.
- candiodari 9y agoOk, I can understand how a bloom filter can be replaced by a neural network predictive model. You could actually train it while stuff gets added. This would make adding somewhat more expensive, but ... Ah so it appears they're advocating using neural networks as index functions to sorted arrays (hashmaps are simply sorted by hash instead of by something in the data). So what they do is they take a FIXED set of data that you want to quickly lookup in, already sorted, train a model (2 layer 32 width, relu activation is one architecture, but they also train sequences of models, HUGE changes to error (as the cost of max and min error are huge, you minimize max error rather than average error)). They have the following brilliant insight : an index over a database (which gives the position of the data given the search key) is a CDF (cumulative distribution function) ! That's brilliant ! Of course it is ! And of course, this is Google. Once you have an index trained (which is a linear operation), you can translate the neural network model directly into C++, and compile it into machine instructions that don't depend on anything like tensorflow libraries. The resulting code can be pasted into anything you want. This may work fast, but seems less then entirely practical ... although I guess you could do the same in Java far easier and you could just include that code. Paper here: http://learningsys.org/nips17/assets/slides/dean-nips17.pdf http://learningsys.org/nips17/assets/slides/dean-nips17.pdf
- deleted 9y ago[deleted]
- frankmcsherry 9y agoIn case anyone wants to check out some pre-history, back in 2002 Manfred Warmuth et al.[0] were using learning (Weighted Majority) to drive systems components like cache replacement policy. I'm not sure where the work went from there, but add it to the pile of techniques. [0]: https://users.soe.ucsc.edu/~sbrandt/papers/NIPS02.pdf https://users.soe.ucsc.edu/~sbrandt/papers/NIPS02.pdf
- eternalban 9y agoThanks for the link. Very interesting. I found this [1] from 2015. Reading your cite, the practical issue seems to me to be that the optimizer's memory footprint costs may in fact negate any benefit (e.g. ~40% over LRU) obtained in reducing cache misses. My gut feeling is that this approach (for online systems) may work best with a hardware component (a card hosting the 'experts' and their virtual model e.g. the "virtual cache"). The distributed variant also seems worth exploring. [1]: https://arxiv.org/pdf/1403.0388.pdf https://arxiv.org/pdf/1403.0388.pdf
- 31reasons 9y agoSo basically it will replace all heuristics/greedy optimization algorithms. I am wondering if ML can come up with better sorting algorithms, or I guess when you can use ML for end strategy of optimization you don't have to sort!
- killjoywashere 9y agoI think the genomics folks have been onboard with this for a couple years now.
- laythea 9y ago"replacing human engineering" Good summary, but someone still has to write the machine intelligence!
- p1esk 9y agoGreat comment. Fits most AGI discussions.