I am in a project to process an image using CUDA. The project is simply an addition or subtraction of the image.
May I ask your professional opinion, which is best and what would be the advantages and disadvantages of those two?
I appreciate everyone's opinions and/or suggestions since this project is very important to me.
General answer: It doesn't matter. Use the language you're more comfortable with.
Keep in mind, however, that pycuda is only a wrapper around the CUDA C interface, so it may not always be up-to-date, also it adds another potential source of bugs, …
Python is great at rapid prototyping, so I'd personally go for Python. You can always switch to C++ later if you need to.
If the rest of your pipeline is in Python, and you're using Numpy already to speed things up, pyCUDA is a good complement to accelerate expensive operations. However, depending on the size of your images and your program flow, you might not get too much of a speedup using pyCUDA. There is latency involved in passing the data back and forth across the PCI bus that is only made up for with large data sizes.
In your case (addition and subtraction), there are built-in operations in pyCUDA that you can use to your advantage. However, in my experience, using pyCUDA for something non-trivial requires knowing a lot about how CUDA works in the first place. For someone starting from no CUDA knowledge, pyCUDA might be a steep learning curve.
Take a look at openCV, it contains a lot of image processing functions and all the helpers to load/save/display images and operate cameras.
It also now supports CUDA, some of the image processing functions have been reimplemented in CUDA and it gives you a good framework to do your own.
Alex's answer is right. The amount of time consumed in the wrapper is minimal. Note that PyCUDA has some nice metaprogramming constructs for generating kernels which might be useful.
If all you're doing is adding or subtracting elements of an image, you probably shouldn't use CUDA for this at all. The amount of time it takes to transfer back and forth across the PCI-E bus will dwarf the amount of savings you get from parallelism.
Any time you deal with CUDA, it's useful to think about the CGMA ratio (computation to global memory access ratio). Your addition/subtraction is only 1 float point operation for 2 memory accesses (1 read and 1 write). This ends up being very lousy from a CUDA perspective.
Related
Has anybody tried developing a SLAM system that uses deep learned features instead of the classical AKAZE/ORB/SURF features?
Scanning recent Computer Vision conferences, there seem to be quite a few reports of successful usage of neural nets to extract features and descriptors, and benchmarks indicate that they may be more robust than their classical computer vision equivalent. I suspect that extraction speed is an issue, but assuming one has a decent GPU (e.g. NVidia 1050), is it even feasible to build a real-time SLAM system running say at 30FPS on 640x480 grayscale images with deep-learned features?
This was a bit too long for a comment, so that's why I'm posting it as an answer.
I think it is feasible, but I don't see how this would be useful. Here is why (please correct me if I'm wrong):
In most SLAM pipelines, precision is more important than long-term robustness. You obviously need your feature detections/matchings to be precise to get reliable triangulation/bundle (or whatever equivalent scheme you might use). However, the high level of robustness that neural networks provide is only required with systems that do relocalization/loop closure on long time intervals (e.g. need to do relocalization in different seasons etc). Even in such scenarios, since you already have a GPU, I think it would be better to use a photometric (or even just geometric) model of the scene for localization.
We don't have any reliable noise models for the features that are detected by the neural networks. I know there have been a few interesting works (Gal, Kendall, etc...) for propagating uncertainties in deep networks, but these methods seem a bit immature for deployment ins SLAM systems.
Deep learning methods are usually good for initializing a system, and the solution they provide needs to be refined. Their results depend too much on the training dataset, and tend to be "hit and miss" in practice. So I think that you could trust them to get an initial guess, or some constraints (e.g. like in the case of pose estimation: if you have a geometric algorithm that drifts in time, then you can use the results of a neural network to constrain them. But I think that the absence of a noise model as mentioned previously will make the fusion a bit difficult here...).
So yes, I think that it is feasible and that you can probably, with careful engineering and tuning produce a few interesting demos, but I wouldn't trust it in real life.
I am aware that C/C++ is a lower-level language and generates relatively optimized machine code when we compare with any other high-level language. But I guess there is pretty much more than that, which is also evident from the practice.
When I do simple calculations like montecarlo averaging of a Gaussian sample collection or so, I see there is not much of a difference between a C++ implementation or MATLAB implementation, sometimes in fact MATLAB performs a bit better in time.
When I move on to larger scale simulations with thousands of lines of code, slowly the real picture shows up. C++ simulations show superior performance like 100x better in time complexity than an equivalent MATLAB implementation.
The code in C++ most of the times, is pretty much serial and no hi-fi optimization is done explicitly. Whereas, as per my awareness, MATLAB inherently does a lot of optimization. This shows up for example when I try to generate a huge chunk of random samples, where as the equivalent in C++ using some library like IT++/GSL/Boost performs relatively slower (the algorithm used is the same namely mt19937).
My question is simply to know if there is a simpler tradeoff between MATLAB/C++ in performance. Is it just like what people say, "Whenever you can, C/C++ is the better"(The frequently experienced)?. In a different perspective, "What is MATLAB good for, other than comfort?"
By the way, I don't see coding efficiency parameter being significant here, thinking of the same programmer in both cases. And also, I think the other alternatives like python,R are not relevant here. But dependence on the specific libraries we use should be interesting.
[I am a phd student in Coding Theory in communication systems. I do simulations using matlab/C++ all the time, and have reasonable experience of coding few 10K's of lines in both cases]
I have been using Matlab and C++ for about 10 years. For every numerical algorithms implemented for my research, I always start from prototyping with Matlab and then translate the project to C++ to gain a 10x to 100x (I am not kidding) performance improvement. Of course, I am comparing optimized C++ code to the fully vectorized Matlab code. On average, the improvement is about 50x.
There are lot of subtleties behind both of the two programming languages, and the following are some misunderstandings:
Matlab is a script language but C++ is compiled
Matlab uses JIT compiler to translate your script to machine code, you can improve your speed at most by a factor 1.5 to 2 by using the compiler that Matlab provides.
Matlab code might be able to get fully vectorized but you have to optimize your code by hand in C++
Fully vectorized Matlab code can call libraries written in C++/C/Assembly (for example Intel MKL). But plain C++ code can be reasonably vectorized by modern compilers.
Toolboxes and routines that Matlab provides should be very well tuned and should have reasonable performance
No. Other than linear algebra routines, the performance is generally bad.
The reasons why you can gain 10x~100x performance in C++ comparing to vectorized Matlab code:
Calling external libraries (MKL) in Matlab costs time.
Memory in Matlab is dynamically allocated and freed. For example, small matrices multiplication:
A = B*C + D*E + F*G
requires Matlab to create 2 temporary matrices. And in C++, if you allocate your memory before hand, you create NONE. And now imagine you loop that statement for 1000 times. Another solution in C++ is provided by C++11 Rvalue reference. This is the one of the biggest improvement in C++, now C++ code can be as fast as plain C code.
If you want to do parallel processing, Matlab model is multi-process and the C++ way is multi-thread. If you have many small tasks needing to be parallelized, C++ provides linear gain up to many threads but you might have negative performance gain in Matlab.
Vectorization in C++ involves using intrinsics/assembly, and sometimes SIMD vectorization is only possible in C++.
In C++, it is possible for an experienced programmer to completely avoid L2 cache miss and even L1 cache miss, hence pushing CPU to its theoretical throughput limit. Performance of Matlab can lag behind C++ by a factor of 10x due to this reason alone.
In C++, computational intensive instructions sometimes can be grouped according to their latencies (code carefully in assembly or intrinsics) and dependencies (most of time is done automatically by compiler or CPU hardware), such that theoretical IPC (instructions per clock cycle) could be reached and CPU pipelines are filled.
However, development time in C++ is also a factor of 10x comparing to Matlab!
The reasons why you should use Matlab instead of C++:
Data visualization. I think my career can go on without C++ but I won't be able to survive without Matlab just because it can generate beautiful plots!
Low efficiency but mathematically robust build-in routines and toolboxes. Get the correct answer first and then talk about efficiency. People can make subtle mistakes in C++ (for example implicitly convert double to int) and get sort of correct results.
Express your ideas and present your code to your colleagues. Matlab code is much easier to read and much shorter than C++, and Matlab code can be correctly executed without compiler. I just refuse to read other people's C++ code. I don't even use C++ GNU scientific libraries because the code quality is not guaranteed. It is dangerous for a researcher/engineer to use a C++ library as a black box and take the accuracy as granted. Even for commercial C/C++ libraries, I remember Intel compiler had a sign error in its sin() function last year and numerical accuracy problems also occurred in MKL.
Debugging Matlab script with interactive console and workspace is a lot more efficient than C++ debugger. Finding an index calculation bug in Matlab could be done within minutes, but it could take hours in C++ figuring out why the program crashes randomly if boundary check is removed for the sake of speed.
Last but not the least:
Because once Matlab code is vectorized, there is not much left for a programmer to optimize, Matlab code performance is much less sensitive to the quality of the code comparing with C++ code. Therefore it is best to optimize computation algorithms in Matlab, and marginally better algorithms normally have marginally better performance in Matlab. On the other hand, algorithm test in C++ requires decent programmer to write algorithms optimized more or less in the same way, and to make sure the compiler does not optimize the algorithms differently.
My recent experience in C++ and Matlab:
I made several large Matlab data analysis tools in the past year and suffered from the slow speed of Matlab. But I was able to improve my Matlab program speed by 10x through the following techniques:
Run/profile the Matlab script, re-implement critical routines in C/C++ and compile with MEX. Critical routines are mostly likely logically simple but numerically heavy. This improves speed by 5x.
Simplify ".m" files shipped with Matlab tool boxes by commenting all unnecessary safety checks and output parameter computations. Please be reminded that the modified code cannot be distributed with the rest of the user scripts. This improves speed by another 2x (after C/C++ and MEX).
The improved code is ~98% in Matlab and ~2% in C++.
I believe it is possible to improve the speed by another 2x (total 20x) if the entire tool is coded in C++, this is ~100x speed improvement of the computation routines. The hard drive I/O will then dominate the program run time.
Question for Mathworks engineers:
When Matlab code is fully vectorized, one of the performance limiting factor is the matrix indexing operation. For instance, a finite difference operation needs to be performed on Matrix A which has a dimension of 5000x5000:
B = A(:,2:end)-A(:,1:end-1)
The matrix indexing operation makes the Matlab code multiple times slower than the C++ code. Can the matrix indexing performance be improved?
In my experience (several years of Computer Vision and image processing in both languages) there is no simple answer to this question, as Matlab performance depends strongly (and much more than C++ performance) on your coding style.
Generally, Matlab wraps the classic C++ / Fortran based linear algebra libraries. So anything like x = A\b is going to be very fast. Also, Matlab does a good job in choosing the most efficient solver for these types of problems, so for x = A\b Matlab will look at the size of your matrices and chose the appropriate low-level routines.
Matlab also shines in data manipulation of large matrices if you "vectorize" your code, i.e. if you avoid for loops and use index arrays or boolean arrays to access your data. This stuff is highly optimised.
For other routines, some are written in Matlab code, while others point to a C/C++ implementation (e.g. the Delaunay stuff). You can check this yourself by typing edit some_routine.m. This opens the code and you see whether it is all Matlab or just a wrapper for something compiled.
Matlab, I think, is primarily for comfort - but comfort translates to coding time and ultimately money which is why Matlab is used in the industry. Also, it is easy to learn for engineers from other fields than computer science, with little training in programming.
As a PhD Student too, and a 10years long Matlab user, I'm glad to share my POV:
Matlab is a great tool for developing and prototyping algorithms, especially when dealing with GUIs, high-level analysis (Frequency Domain, LS Optimization etc.): fast coding, powerful syntaxis (think about [],{},: etc.).
As soon as your processing chain is more stable and defined and data dimensions grows move to C/C++.
The main Matlab limit rises when considering its language is script-like: as long as you avoid any cycle (using arrayfun, cellfun or other matrix procedures) performances are high since the called subroutine is again in C/C++.
Your question is difficult to answer. In general C++ is faster, but if make use of the well written algorithms of Matlab it can outperform C++. In some cases Matlab can parallelize your code which has to be done manually in many cases for C++. Mathlab can kind of export C++ code.
So my conclusion is, that you have to measure the performance of both programs to get an answer. But then you compare your two implementations and not Matlab and C++ in general.
Matlab does very well with linear algebra and array/matrix operations, since they seem to have been doing some extra optimizations on the underlying operations - if you want to beat Matlab there, you would need a similarly optimized BLAS/LAPACK library.
As an interpreted language, Matlab loses time whenever a Matlab function is called, due to internal overhead, which traditionally meant that Matlab loops were slow. This has been alleviated somewhat in recent years thanks to significant improvement in the JIT compiler (search for "performance" questions on Matlab on SO for examples). As a consequence of the function call overhead, all Matlab functions that have not been implemented in C/C++ behind the scenes (call edit functionName to see whether it's written in Matlab) risks being slower than a C/C++ counterpart.
Finally, Matlab attempts to be user friendly, and may do "unnecessary" input checking that can take time (due to function call overhead). For example, if you know that ismember gets sorted inputs, you can call ismembc directly (the behind-the-scene compiled function), saving quite a bit of time.
I think you can consider the difference in four folds at least.
Compiled vs Interpreted
Strongly-typed vs Dynamically-typed
Performance vs Fast-prototyping
Special strength
For 1-3 can be easily generalized into comparison between two family of programming languages.
For 4, MATLAB is optimized for matrix operations. So if you can vectorize more code in MATLAB, the performance can be drastically boosted. Conversely, if many loops are required, never hesitate to use C++ or create a mex file.
It is a difficult quesion after all.
I saw a 5.5x speed improvement when switching from MATLAB to C++. This was for a robot controller- lots of loops and ode solving. I spent many hours trying to optimize the MATLAB code, hardly any time optimizing the C++ (I'm sure it could have been 10x faster with a little more effort).
However, it was easy to add a GUI for the MATLAB code, so I still use it more often. Like others have said, it was nice to prototype first on MATLAB. That made the implementation on C++ much simpler.
Besides the speed of the final program, you should also take into account the total development time of your code, ie., not only the time to write, but also to debug, etc. Matlab (and its open-source counterpart, Octave) can be good for quick prototyping due to its visualisation capabilities.
If you're using straight C++ (ie. no matrix libraries), it may take you much longer to write C++ code that's equivalent to Matlab code (eg. there might be no point in spending 10 hours writing C++ code that only runs 10 seconds quicker, compared to a Matlab program that took 5 minutes to write).
However, there are dedicated C++ matrix libraries, such as Armadillo, which provide a Matlab-like API. This can be useful for writing performance critical code that can be called from Matlab, or for converting Matlab code into "real" programs.
Some Matlab code uses standard linear algebra fictions with multithreading built into it. So, it appears that they are faster than a sequential C code.
GPU Compute Programmers,
I have a C++ program which currently relies on the ACML (LAPACK) to invert and multiple fairly large matrices of single precision fp values (E.g. 4,000 x 4,000). These matrices are very sparse although they do not always fit nicely into a diagonal matrix so I cannot presently reduce them. The other thing about this program is I have to do this invert and multiply several times (serially) as part of a Newton Rapson. However, I have several thousand permutations which can be done in parallel, each with a small change to the matrix before again calculating and inverting the Jacobian. This is all single precision fp, and seems perfectly suited for the GPU. My question is this...
I suspect I will need to use the AMD Accelerated Parallel Processing Math Libraries (APPML) for OpenGL as that is the only thing (non-CUDA, I want to be GPU agnostic) I know of which is available with BLAS functionality. My problem is I do not see the LAPACK dgetrf and dgetri functions included in APPML (yes, these are fp64 but I don't need that precision). Would C++ AMP be a better alternative? I am very interested in HSA features of passing pointers rather than copying data as there is a lot of data in flight here and some calculations still are done on the CPU. I believe that copy overhead would kill me otherwise. Ultimately, performance is the key and I want to make the right architectural decisions to set myself up for the most performance I can wring out of HSA GPUs coming out over the next 6 months.
I am using VS 2013 Ultimate preview and would be able to take advantage of C++ AMP for these HSA capabilities. I just want to make sure I am making the right long term architectural decision now while my program is in its infancy. Here is a link and snippet from some interesting data I found on Anandtech:
http://anandtech.com/show/7118/windows-81-and-vs2013-bring-gpu-computing-updates-to-direct3d-and-c-amp-
C++ AMP, Microsoft's C++ extension for GPU computing, has also been updated with the upcoming VS2013. I think the biggest feature update is that C++ AMP programs will also gain a shared memory feature on APUs/SoCs where the compiler and runtime will be able to eliminate extra data copies between CPU and GPU. This feature will also be available only on Windows 8.1 and it is likely built on top of the "map default buffer" as Microsoft's AMP implementation uses Direct3D under the hood. C++ AMP also brings some other nice additions including enhanced texture support and better debugging abilities.
Any thoughts, additional questions or discussion would be greatly appreciated!
Right now I'm developing some application using OpenCV API (C++). This application does processing with video.
On the PC everything works really fast. And today I decided to port this application on Android (to use camera as videoinput). Fortunately, there's OpenCV for Android so I just added my native code to sample Android application. Everything works fine except perfomance. I benchmarked my application and found that application works with 4-5 fps, what is actually not acceptable (my device has singlecore 1ghz processor) - I want it to work with about 10 fps.
Does it make a sence to fully rewrite my application on C? I know that using such things as std::vector is much comfortable for developer, but I don't care about it.
It seems that OpenCV's C interface has same functions/methods as C++ interface.
I googled this question but didn't find anything.
Thanks for any advice.
I've worked quite a lot with Android and optimizations (I wrote a video processing app that processes a frame in 4ms) so I hope I will give you some pertinent answers.
There is not much difference between the C and C++ interface in OpenCV. Some of the code is written in C, and has a C++ wrapper, and some viceversa. Any significant differences between the two (as measured by Shervin Emami) are either regressions, bug fixes or quality improvements. You should stick with the latest OpenCV version.
Why not rewrite?
You will spend a good deal of time, which you could use much better. The C interface is cumbersome, and the chance to introduce bugs or memory leaks is high. You should avoid it, in my opinion.
Advice for optimization
A. Turn on optimizations.
Both compiler optimizations and the lack of debug assertions can make a big difference in your running time.
B. Profile your app.
Do it first on your computer, since it is much easier. Use visual studio profiler, to identify the slow parts. Optimize them. Never optimize because you think is slow, but because you measure it. Start with the slowest function, optimize it as much as possible, then take the second slower. Measure your changes to make sure it's indeed faster.
C. Focus on algorithms.
A faster algorithm can improve performance with orders of magnitude (100x). A C++ trick will give you maybe 2x performance boost.
Classical techniques:
Resize you video frames to be smaller. Often you can extract the information from a 200x300px image, instead of a 1024x768. The area of the first one is 10 times smaller.
Use simpler operations instead of complicated ones. Use integers instead of floats. And never use double in a matrix or a for loop that executes thousands of times.
Do as little calculation as possible. Can you track an object only in a specific area of the image, instead of processing it all for all the frames? Can you make a rough/approximate detection on a very small image and then refine it on a ROI in the full frame?
D. Use C where it matters
In loops, it may make sense to use C style instead of C++. A pointer to a data matrix or a float array is much faster than mat.at or std::vector<>. Often the bottleneck is a nested loop. Focus on it. It doesn't make sense to replace vector<> all over the place and spaghettify your code.
E. Avoid hidden costs
Some OpenCV functions convert data to double, process it, then convert back to the input format. Beware of them, they kill performance on mobile devices. Examples: warping, scaling, type conversions. Also, color space conversions are known to be lazy. Prefer grayscale obtained directly from native YUV.
F. Use vectorization
ARM processors implement vectorization with a technology called NEON. Learn to use it. It is powerful!
A small example:
float* a, *b, *c;
// init a and b to 1000001 elements
for(int i=0;i<1000001;i++)
c[i] = a[i]*b[i];
can be rewritten as follows. It's more verbose, but much faster.
float* a, *b, *c;
// init a and b to 1000001 elements
float32x4_t _a, _b, _c;
int i;
for(i=0;i<1000001;i+=4)
{
a_ = vld1q_f32( &a[i] ); // load 4 floats from a in a NEON register
b_ = vld1q_f32( &b[i] );
c_ = vmulq_f32(a_, b_); // perform 4 float multiplies in parrallel
vst1q_f32( &c[i], c_); // store the four results in c
}
// the vector size is not always multiple of 4 or 8 or 16.
// Process the remaining elements
for(;i<1000001;i++)
c[i] = a[i]*b[i];
Purists say you must write in assembler, but for a regular programmer that's a bit daunting. I had good results using gcc intrinsics, like in the above example.
Another way to jump-start is to convrt handcoded SSE-optimized code in OpenCV into NEON. SSE is the NEON equivalent in Intel processors, and many OpenCV functions use it, like here. This is the image filtering code for uchar matrices (the regular image format). You should't blindly convert instructions one by one, but take it as an example to start with.
You can read more about NEON in this blog and the following posts.
G. Pay attention to image capture
It can be surprisingly slow on a mobile device. Optimizing it is device and OS specific.
Before making any decision like this, you should profile your code to locate the hotspots in your code. Without this information, any changes you make to speed things up will be guesswork. Have you tried this Android NDK profiler?
There is some performance tests done by shervin imami on his website. You can check it to get some ideas.
http://www.shervinemami.info/timingTests.html
Hope it helps.
(And also, it would be nice if you share your own findings somewhere if you get any way for performance boost.)
I guess the question needs to be formulated to: is C faster than C++? and the answer is NO. Both are compiled to the native machine language and C++ is designed to be as fast as C
As for the STL (espeically ISO standard) are also designed and taken care that they are as fast as pointers + they offer flexibility.
The only reason to use C is that your platform doesn't support C++
In my humble openion, don't convert everything to C, as you'll probably get almost the same performance. and try instead to improve your code or use other functionalities of opencv to do what you want.
Not convinced? well then write a simple function, once in C and once in C++, and run it in a loop of 100 million times and measure the time yourself. Maybe this helps you taking the right decision
I've never used C or C++ in Android. But in a PC you can get C++ to run as fast as C code (sometimes even faster). Most of C++ was designed specifically to allow more features, but not at the cost of speed (Templates are solved at compile time). Most compilers are pretty good at optimizing your code, and your std::vector calls will be inlined and the code will be almost the same as using a native C array.
I'd suggest you look for another way of improving your performance. Maybe there are some multimedia hardware extensions in the Android you can get access to and use to optimize the code.
I noticed in multiple tests that:
C interface (IplImage) is a number of times faster when accessing the pixels directly instead of using Mat.at(x,y) method, when I converted my C++ application to C, I had a 3x performance increase in my blob detection routine
C++ interface crashes in certain routines when called from external applications (e.g. LabView) whereas it works when calling the same routines in C. Example of this is FindContours and cvFindContours
C is far more compatible with embedded devices. However, I have not done anything in this field yet.
I had similar problems on IOS devices and discussion Maximum speed from IOS/iPad/iPhone includes some hints applicable to other mobile platforms too.
I wrote a simple program(calculating the number of steps for the numbers 1-10^10 using the collatz conjecture)in both python and c++. They were nearly identical, and neither were written for multithreading. I ran the one in python and according to my system manager, one core went straight to 100% usage, the others staying the same. I ran the c++ program and the cores stayed at their same fluctuating between 10 and 15% usage states, never really changing. They both completed around the same time, within seconds. Could someone explain to me why this is happening?
Python is, in general, quite slow at raw number crunching. This is because it uses its full, general purpose object model for everything, including numbers. You can contrast this with Java and C++, which have "native types" which don't offer any of the niceties of a real class (methods, inheritance, data attributes, etc), but do offer access to the raw speed of the underlying CPU.
So, x = a + b in C++ generally has far less work to do at runtime than x = a + b in Python, despite the superficially identical syntax. Python's unified object model is one of the things that makes it comparatively easy to use, but it can have a downside on the raw speed front.
There are multiple alternative approaches to recovering that lost speed:
use a custom C extension to drop back down to raw CPU calculations and recover the speed directly
use an existing numeric library to do the same thing
use a just-in-time compiler (e.g. via the psyco project or PyPy)
use multiprocessing or concurrent.futures to take advantage of multiple cores, or even a distributed computing library to make use of multiple machines
P.S. This is a much better question now that the algorithm is described :)