I am installing tensorflow from this link.
When I run the ./configurecommand, I see following terms
XLA JIT
GDR
VERBS
OpenCL
Can somebody explain in simple language, what these terms mean and what are they used for?
XLA stands for 'Accelerated Linear Algebra'. The XLA page states that: 'XLA takes graphs ("computations") [...] and compiles them into machine instructions for various architectures." As far as I understand will this take the computation you define in tensorflow and compile it. Think of producing code in C and then running it through the C compiler for the CPU and load the resulting shared library with the code for the full computation instead of making separate calls from python to compiled functions for each part of your computation. Theano does something like this by default. JIT stands for 'just in time compiler', i.e. the graph is compiled 'on the fly'.
GDR seems to be support for exchanging data between GPUs on different servers via GPU direct. GPU direct makes it possible that e.g. the network card which receives data from another server over the network writes directly into the local GPU's memory without going through the CPU or main memory.
VERBS refers to the Infiniband Verbs application programming interface ('library'). Infiniband is a low latency network used in many supercomputers for example. This can be used when you want to run tensorflow on more than one server for communication between them. The Verbs API is to Infiniband what the Berkeley Socket API is to TCP/IP communication (although there are many more communication options and different semantics optimized for performance with Verbs).
OpenCL is a programming language suited for executing parallel computing tasks on CPU and non-CPU devices such as GPUs, with a C like syntax. With respect to C however there are certain restrictions such as no support for recursion etc. One could probably say that OpenCL is to AMD what CUDA is to NVIDIA (although also OpenCL is also used by other companies like ALTERA).
Related
When running a simulation in gem5, I can select a CPU with fs.py --cpu-type.
This option can also show a list of all CPU types if I use an invalid CPU type such as fs.py --cpu-type.
What is the difference between those CPU types and which one should I choose for my experiment?
Question inspired by: https://www.mail-archive.com/gem5-users#gem5.org/msg16976.html
An overview of the CPU types can be found at: https://cirosantilli.com/linux-kernel-module-cheat/#gem5-cpu-types
In summary:
simplistic CPUs (derived from BaseSimpleCPU): for example AtomicSimpleCPU (the default one). They have no CPU pipeline, and therefor are completely unrealistic. However, they also run much faster. Therefore,they are mostly useful to boot Linux fast and then checkpoint and switch to a more detailed CPU.
Within the simple CPUs we can notably distinguish:
AtomicSimpleCPU: memory requests finish immediately
TimingSimpleCPU: memory requests actually take time to go through to the memory system and return. Since there is no CPU pipeline however, the simulated CPU stalls on every memory request waiting for a response.
An alternative to those is to use KVM CPUs to speed up boot if host and guest ISA are the same, although as of 2019, KVM is less stable as it is harder to implement and debug.
in-order CPUs: derived from the generic MinorCPU by parametrization, Minor stands for In Order:
for ARM: HPI is made by ARM and models a "(2017) modern in-order Armv8-A implementation". This is your best in-order ARM bet.
out-of-order CPUs, derived from the generic DerivO3CPU by parametrization, O3 stands for Out Of Order:
for ARM: there are no models specifically published by ARM as of 2019. The only specific O3 model available is ex5_big for an A15, but you would have to verify its authors claims on how well it models the real core A15 core.
If none of those are accurate enough for your purposes, you could try to create your own in-order/out-of-order models by parametrizing MinorCPU / DerivO3CPU like HPI and ex5_big do, although this could be hard to get right, as there isn't generally enough public information on non-free CPUs to do this without experiments or reverse engineering.
The other thing you will want to think about is the memory system model. There are basically two choices: classical vs Ruby, and within Ruby, several options are available, see also: https://cirosantilli.com/linux-kernel-module-cheat/#gem5-ruby-build
Since I only have an AMD A10-7850 APU, and do not have the funds to spend on a $800-$1200 NVIDIA graphics card, I am trying to make due with the resources I have in order to speed up deep learning via tensorflow/keras.
Initially, I used a pre-compiled version of Tensorflow. InceptionV3 would take about 1000-1200 seconds to compute 1 epoch. It has been painfully slow.
To speed up calculations, I first self-compiled Tensorflow with optimizers (using AVX, and SSE4 instructions). This lead to a roughly 40% decrease in computation times. The same computations performed above now only take about 600 seconds to compute. It's almost bearable - kind of like you can watch paint dry.
I am looking for ways to further decrease computation times. I only have an integrated AMD graphics card that is part of the APU. (How) (C/c)an I make use of this resource to speed up computation even more?
More generally, let's say there are other people with similar monetary restrictions and Intel setups. How can anyone WITHOUT discrete NVIDIA cards make use of their integrated graphics chips or otherwise non-NVIDIA setup to achieve faster than CPU-only performance? Is that possible? Why/Why not? What needs to be done to achieve this goal? Or will this be possible in the near future (2-6 months)? How?
After researching this topic for a few months, I can see 3.5 possible paths forward:
1.) Tensorflow + OpenCl as mentioned in the comments above:
There seems to be some movement going on this field. Over at Codeplay, Lukasz Iwanski just posted a comprehensive answer on how to get tensorflow to run with opencl here (I will only provide a link as stated above because the information might change there): https://www.codeplay.com/portal/03-30-17-setting-up-tensorflow-with-opencl-using-sycl
The potential to use integrated graphics is alluring. It's also worth exploring the use of this combination with APUs. But I am not sure how well this will work since OpenCl support is still early in development, and hardware support is very limited. Furthermore, OpenCl is not the same as a handcrafted library of optimized code. (UPDATE 2017-04-24: I have gotten the code to compile after running into some issues here!) Unfortunately, the hoped for speed improvements ON MY SETUP (iGPU) did not materialize.
CIFAR 10 Dataset:
Tensorflow (via pip ak unoptimized): 1700sec/epoch at 390% CPU
utilization.
Tensorflow (SSE4, AVX): 1100sec/epoch at 390% CPU
utilization.
Tensorflow (opencl + iGPU): 5800sec/epoch at 150% CPU
and 100% GPU utilization.
Your mileage may vary significantly. So I am wondering what are other people getting relatively speaking (unoptimized vs optimized vs opencl) on your setups?
What should be noted: opencl implementation means that all the heavy computation should be done on the GPU. (Updated on 2017/4/29) But in reality this is not the case yet because some functions have not been implemented yet. This leads to unnecessary copying back and forth of data between CPU and GPU ram. Again, imminent changes should improve the situation. And furthermore, for those interested in helping out and those wanting to speed things up, we can do something that will have a measurable impact on the performance of tensorflow with opencl.
But as it stands for now: 1 iGPU << 4 CPUS with SSE+AVX. Perhaps beefier GPUs with larger RAM and/or opencl 2.0 implementation could have made a larger difference.
At this point, I should add that similar efforts have been going on with at least Caffe and/or Theano + OpenCl. The limiting step in all cases appears to be the manual porting of CUDA/cuDNN functionality to the openCl paradigm.
2.) RocM + MIOpen
RocM stands for Radeon Open Compute and seems to be a hodgepodge of initiatives that is/will make deep-learning possible on non-NVIDIA (mostly Radeon devices). It includes 3 major components:
HIP: A tool that converts CUDA code to code that can be consumed by AMD GPUs.
ROCk: a 64-bit linux kernel driver for AMD CPU+GPU devices.
HCC: A C/C++ compiler that compiles code into code for a heterogeneous system architecture environment (HSA).
Apparently, RocM is designed to play to AMDs strenghts of having both CPU and GPU technology. Their approach to speeding up deep-learning make use of both components. As an APU owner, I am particularly interested in this possibility. But as a cautionary note: Kaveri APUs have limited support (only integrated graphcs is supported). Future APUs have not been released yet. And it appears, there is still a lot of work that is being done here to bring this project to a mature state. A lot of work will hopefully make this approach viable within a year given that AMD has announced their Radeon Instinct cards will be released this year (2017).
The problem here for me is that that RocM is providing tools for building deep learning libraries. They do not themselves represent deep learning libraries. As a data scientist who is not focused on tools development, I just want something that works. and am not necessarily interested in building what I want to then do the learning. There are not enough hours in the day to do both well at the company I am at.
NVIDIA has of course CUDA and cuDNN which are libaries of hand-crafted assembler code optimized for NVIDIA GPUs. All major deep learning frameworks build on top of these proprietary libraries. AMD currently does not have anything like that at all.
I am uncertain how successfully AMD will get to where NVIDIA is in this regard. But there is some light being shone on what AMDs intentions are in an article posted by Carlos Perez on 4/3/2017 here. A recent lecture at Stanford also talks in general terms about Ryzen, Vega and deep learning fit together. In essence, the article states that MIOpen will represent this hand-crafted library of optimized deep learning functions for AMD devices. This library is set to be released in H1 of 2017. I am uncertain how soon these libraries would be incorporated into the major deep learning frameworks and what the scope of functional implementation will be then at this time.
But apparently, AMD has already worked with the developers of Caffe to "hippify" the code basis. Basically, CUDA code is converted automatically to C code via HIP. The automation takes care of the vast majority of the code basis, leaving only less than 0.5% of code had to be changed and required manual attention. Compare that to the manual translation into openCl code, and one starts getting the feeling that this approach might be more sustainable. What I am not clear about is where the lower-level assembler language optimization come in.
(Update 2017-05-19) But with the imminent release of AMD Vega cards (the professional Frontier Edition card not for consumers will be first), there are hints that major deep learning frameworks will be supported through the MIOpen framework. A Forbes article released today shows the progress MiOpen has taken over just the last couple of months in terms of performance: it appears significant.
(Update 2017-08-25) MiOpen has officially been released. We are no longer talking in hypotheticals here. Now we just need to try out how well this framework works.
3.) Neon
Neon is Nervana's (now acquired by Intel) open-source deep-learning framework. The reason I mention this framework is that it seems to be fairly straightforward to use. The syntax is about as easy and intuitive as Keras. More importantly though, this framework has achieved speeds up to 2x faster than Tensorflow on some benchmarks due to some hand-crafted assembler language optimization for those computations. Potentially, cutting computation times from 500 secs/epoch down to 300 secs/epoch is nothing to sneeze at. 300 secs = 5 minutes. So one could get 15 epochs in in an hour. and about 50 epochs in about 3.5 hours! But ideally, I want to do these kinds of calculations in under an hour. To get to those levels, I need to use a GPU, and at this point, only NVIDIA offers full support in this regard: Neon also uses CUDA and cuDNN when a GPU is available (and of course, it has to be an NVIDIA GPU). If you have access other Intel hardware this is of course a valid way to pursue. Afterall, Neon was developed out of a motivation to get things to work optimally also on non-NVIDIA setups (like Nervana's custom CPUs, and now Intel FPGAs or Xeon Phis).
3.5.) Intel Movidius
Update 2017-08-25: I came across this article. Intel has released a USB3.0-stick-based "deep learning" accelerator. Apparently, it works with Cafe and allows the user perform common Cafe-based fine-tuning of networks and inference. This is important stressing: If you want to train your own network from scratch, the wording is very ambiguous here. I will therefore assume, that apart from fine-tuning a network, training itself should still be done on something with more parallel compute. The real kicker though is this: When I checked for the pricing this stick costs a mere $79. That's nothing compared to the cost of your average NVIDIA 1070-80(ti) card. If you merely want to tackle some vision problems using common network topologies already available for some related tasks, you can use this stick to fine tune it to your own use, then compile the code and put it into this stick to do inference quickly. Many use cases can be covered with this stick, and for again $79 it could be worth it. This being Intel, they are proposing to go all out on Intel. Their model is to use the cloud (i.e. Nervana Cloud) for training. Then, use this chip for prototype inference or inference where energy consumption matters. Whether this is the right approach or not is left for the reader to answer.
At this time, it looks like deep learning without NVIDIA is still difficult to realize. Some limited speed gains are difficult but potentially possible through the use of opencl. Other initiatives sound promising but it will take time to sort out the real impact that these initiatives will have.
If your platform supports opencl you can look at using it with tensorflow. There is some experimental support for it on Linux at this github repository. Some preliminary instructions are at the documentation section of of this github repository.
I am running a program that does numeric ODE integration in Julia. I am running Windows 10 (64bit), with Intel Core i7-4710MQ # 2.50Ghz (8 logical processors).
I noticed that when my code was running on julia, only max 30% of CPU is in usage. Going into the parallelazation documentation, I started Julia using:
C:\Users\*****\AppData\Local\Julia-0.4.5\bin\julia.exe -p 8 and expected to see improvements. I did not see them however.
Therefore my question is the following:
Is there a special way I have to write my code in order for it to use CPU more efficiently? Is this maybe a limitation posed by my operating system (windows 10)?
I submit my code in the julia console with the command:
include("C:\\Users\\****\\AppData\\Local\\Julia-0.4.5\\13. Fast Filesaving Format.jl").
Within this code I use some additional packages with:
using ODE; using PyPlot; using JLD.
I measure the CPU usage with windows' "Task Manager".
The -p 8 option to julia starts 8 worker processes, and disables multithreading in libraries like BLAS and FFTW so that the workers don't oversubscribe the physical threads on the system – since this kills performance in well-balanced distributed workloads. If you want to get more speed out of -p 8 then you need to distribute work between those workers, e.g. by having each of them do an independent computation, or by having the collaborate on a computation via SharedArrays. You can't just add workers and not change the program. If you are using BLAS (doing lots of matrix multiplies) or FFTW (doing lots of Fourier transforms), then if you don't use the -p flag, you'll automatically get multithreading from those libraries. Otherwise, there is no (non-experimental) user-level threading in Julia yet. There is experimental threading support and version 1.0 will support threading, but I wouldn't recommend that yet unless you're an expert.
I've got a program that takes about 24 hours to run. It's all written in VB.net and it's about 2000 lines long. It's already multi-threaded and this works perfectly (after some sweat and tears). I typically run the processes with 10 threads but I'd like to increase that to reduce processing time, which is where using the GPU comes into it. I've search google for everything related that I can think of to find some info but no luck.
What I'm hoping for is a basic example of a vb.net project that does some general operations then sends some threads to the GPU for processing. Ideally I don't want to have to pay for it. So something like:
'Do some initial processing eg.
dim x as integer
dim y as integer
dim z as integer
x=int(textbox1.text)
y=int(textbox2.text)
z=x*y
'Do some multi-threaded operations on the gpu eg.
'show some output to the user once this has finished.
Any help or links will be much appreciated. I've read plenty of articles about it in c++ and other languages but I'm rubbish at understanding other languages!
Thanks all!
Fraser
The VB.NET compiler does not compile onto the GPU, it compiles down to an intermediate language (IL) that is then just-in-time compiled (JITed) for the target architecture at runtime. Currently only x86, x64 and ARM targets are supported. CUDAfy (see below) takes the IL and translates it into CUDA C code. In turn this is compiled with NVCC to generate code that the GPU can execute. Note that this means that you are limited to NVidia GPUs as CUDA is not supported on AMD.
There are other projects that have taken the same approach, for example a Python to CUDA translator in Copperhead.
CUDAfy - A wrapper on top of the CUDA APIs with additional libraries for FFTs etc. There is also a commercial version. This does actually
CUDAfy Translator
Using SharpDevelop's decompiler ILSpy as basis the translator converts .NET code to CUDA C.
There are other projects to allow you to use GPUs from .NET languages. For example:
NMath - A set of math libraries that can be used from .NET and are GPU enabled.
There may be others but these seem to be the main ones. If you decide to use CUDAfy then you will still need to invest some time in understanding enough of CUDA and how GPUs work to port your algorithm to fit the GPU data-parallel model. Unless it is something that can be done out of the box with one of the math libraries.
It's important to realize that there is still a performance hit for accessing the GPU from a .NET language. You must pay a cost for moving (marshaling) data from the .NET managed runtime into the native runtime. The overhead here largely depends on not only the size but also the type of data and if it can be marshaled without conversion. This is in addition to the cost of moving data from the CPU to the GPU etc.
I'm newbie in GPU programming , and i work on brute force RAR Password Recovery on ATI Stream Processor using brook+ language, but i see that the kernel written in brook+ language doesn't allow any calling to normal functions (except kernel functions) , my questions is :
1) how to use unrar.dll (to unrar archive files) API in this situation? and is this the only way to program RAR password recovery?
2) what about crack and ElcomSoft software that use GPU , how they work ?
3) what exactly the role for the function work inside GPU (ATI Stream processor or CUDA) in this program?
4) is nVidia/CUDA technology is easier/more flexible than ATI/brook+ language ?
1) unrar.dll is a compiled dynamic link library. These execute on the CPU. GPUs have vastly different machine code and a very different execution model, so they can't run dlls.
You could try to implement a callback from the GPU to the CPU via events, or build an x86 interpreter on the GPU, but these would almost certainly run slower than just running on the CPU.
Using unrar.dll is not the only way to program RAR password recovery. You could instead just build your own code for CPU and GPU from scratch.
2) They work by having the CPU code explicitly request that some GPU code run on the GPU.
3) I don't know exactly. I would guess though that it has a GPU program that tries many different combinations, and benefits from having these run in parallel.
4) CUDA is more mature than brook+. brook+ may be just as easy for simple tasks, but isn't as fully featured. For new projects most people would now choose OpenCL over brook+.
(I'm not sure what you're intending to do, but none of the above seems likely to enable anything sinister.)