Usage

The installation is very simple, simply untar an archive and put the executables in your PATH (or use absolute paths). There are also some deb and rpm packages for some distributions. You need strace to make the analyzer work, it should be available on any Linux platform.

The usage of PVS-Studio on Linux should be straightforward. First, you can use the analyzer directly with make and it will detect the invocations of the compiler. For instance, here is the command I used for ETL:

pvs-studio-analyzer trace -- make -j5 debug/bin/etl_test

Note that you can use several threads without issues, which is really great. There does not seem to be any slowdown at this stage, probably only collecting compiler arguments.

This first step creates a strace_out file that will be used by the next stage.

Once, the compilation has been analyzed, you can generate the results with the analyze function, for which you'll need a license. Here is what I did:

pvs-studio-analyzer analyze -l ~/pvs_studio/PVS-Studio.lic -j5

Unfortunately, this didn't work for me:

No compilation units found
Analysis finished in 0:00:00.00

Apparently, it's not able to use the strace_out it generated itself...

Another possibility is to use the compilation database from clang to use PVS-Studio. So I generated my compile_commands.json file again (it was not up to date...) with Bear. And then, you only need to run the analyze step:

pvs-studio-analyzer analyze -l ~/pvs_studio/PVS-Studio.lic -j5

Make sure you have the same compiler configured than the one used to generate the compilation database to avoid errors with compiler arguments.

Unfortunately, this just printed a load of crap on my console:

(L8Pu(]-'Lo8h>uo(_uv(uo2(->'2h_u(uo2(uvU2K h>'o8a=}Lkk;x[G^%cuaa8acr[VS%
$ckUaoc8 c'8>_-o-8>U2cu/kau==-8>c-=cU2]Uf=c U2=u%c&kU__->j}c@uvu2%cJ
(L8Pu(]-'Lo8h>uo(_uv(uo2(->'2h_u(uo2(uvU2K h>'o8a=}Lkk;JVJ^%cuaa8acr[VS%
$ckUaoc8 c'8>_-o-8>U2cu/kau==-8>c-=cU2]Uf=c U2=u%c&kU__->j}c@uvu2%cJ
(L8Pu(]-'Lo8h>uo(_uv(uo2(->'2h_u(uo2(uvU2K h>'o8a=}Lkk;*[G^%cuaa8acr[VS%
$ckUaoc8 c'8>_-o-8>U2cu/kau==-8>c-=cU2]Uf=c U2=u%c&kU__->j}c@uvu2%cJ
(L8Pu(]-'Lo8h>uo(_uv(uo2(->'2h_u(uo2(uvU2K h>'o8a=}Lkk;b[b^%cuaa8acr[VS%
$ckUaoc8 c'8>_-o-8>U2cu/kau==-8>c-=cU2]Uf=c U2=u%c&kU__->j}c@uvu2%cJ
(L8Pu(]-'Lo8h>uo(_uv(uo2(->'2h_u(uo2(uvU2K h>'o8a=}Lkk;[[x^%cuaa8acr[VS%
$ckUaoc8 c'8>_-o-8>U2cu/kau==-8>c-=cU2]Uf=c U2=u%c&kU__->j}c@uvu2%cJ

Pretty nice, isn't it ?

Let's try again in a file:

pvs-studio-analyzer analyze -o results.log -l ~/pvs_studio/PVS-Studio.lic -j5

The time is quite reasonable for the analysis, it took much less time than the compilation time. In total, it took 88 seconds to analyze all the files. It's much faster than the clang static analyzer.

This time it worked, but the log file is not readable, you need to convert it again:

plog-converter -t errorfile -o errors results.log

And finally, you can read the results of the analysis in the errors file.

Results

Overall, PVS-Studio found 236 messages in the ETL library, I was expecting more. I also wish there was an HTML report that include the source code as well as the error message. I had to lookup at the code for each message (you could integrate it in vim and then use the quickfix window to do that). There are some visualization but in things like QtCreator or LibreOffice which I don't have nor want on my computer.

Let's look at the results. For each message, I'll include the message from PVS-Studio and the code if it's relevant.

The first is about using the comma:

include/etl/traits.hpp:674:1: error: V521 Such expressions using the ',' operator are dangerous. Make sure the expression is correct.
include/etl/traits.hpp:674:1: error: V685 Consider inspecting the return statement. The expression contains a comma.

template <typename E>
constexpr std::size_t dimensions(const E& expr) noexcept {
    return (void)expr, etl_traits<E>::dimensions();
}

Here I'm simply using the comma operand to ignore expr to avoid a warning. To make this compile in C++11, you need to do it in one line otherwise it's not a constexpr function. It's probably not perfect to use this construct, but there is no problem here.

There is a bunch of these, let's filter them, it remains 207 warnings. Let's jump to the next one:

include/etl/impl/blas/fft.hpp:29:1: error: V501 There are identical sub-expressions to the left and to the right of the '==' operator: (DFTI_SINGLE) == DFTI_SINGLE

inline void fft_kernel(const std::complex<float>* in, std::size_t s, std::complex<float>* out) {
    DFTI_DESCRIPTOR_HANDLE descriptor;

    void* in_ptr = const_cast<void*>(static_cast<const void*>(in));

    DftiCreateDescriptor(&descriptor, DFTI_SINGLE, DFTI_COMPLEX, 1, s); //Specify size and precision
    DftiSetValue(descriptor, DFTI_PLACEMENT, DFTI_NOT_INPLACE);         //Out of place FFT
    DftiCommitDescriptor(descriptor);                                   //Finalize the descriptor
    DftiComputeForward(descriptor, in_ptr, out);                        //Compute the Forward FFT
    DftiFreeDescriptor(&descriptor);                                    //Free the descriptor
}

Unfortunately, the error is inside the MKL library. Here, I really don't think it's an issue. There is pack of them. I forgot to exclude non-ETL code from the results. Once filter from all dependencies, 137 messages remain.

include/etl/eval_functors.hpp:157:1: warning: V560 A part of conditional expression is always false: !padding.

This is true, but not an issue since padding is a configuration constant that enables the use of padding in vector and matrices. There was 27 of these at different locations and with different configuration variables.

include/etl/op/sub_view.hpp:161:1: note: V688 The 'i' function argument possesses the same name as one of the class members, which can result in a confusion.

This is again true, but not a bug in this particular case. It is still helpful and I ended up changing these to avoid confusion. Again, there was a few of these.

etl/test/src/conv_multi_multi.cpp:23:1: error: V573 Uninitialized variable 'k' was used. The variable was used to initialize itself.

This one is in the test code:

for (size_t k = 0; k < etl::dim<0>(K); ++k) {
    for (size_t i = 0; i < etl::dim<0>(I); ++i) {
        C_ref(k)(i) = conv_2d_valid(I(i), K(k)); // HERE
    }
}

I don't see any error, k is initialized correctly to zero in the first loop. This is a false positive for me. There were several of these in different places. It seems to that the use of the operator() is confusing for PVS-Studio.

include/etl/traits.hpp:703:1: note: V659 Declarations of functions with 'rows' name differ in the 'const' keyword only, but the bodies of these functions have different composition. This is suspicious and can possibly be an error. Check lines: 693, 703.

template <typename E, cpp_disable_if(decay_traits<E>::is_fast)>
std::size_t rows(const E& expr) { //693
    return etl_traits<E>::dim(expr, 0);
}

template <typename E, cpp_enable_if(decay_traits<E>::is_fast)>
constexpr std::size_t rows(const E& expr) noexcept { //703
    return (void)expr, etl_traits<E>::template dim<0>();
}

Unfortunately, this is again a false positive because PVS-Studio failed to recognized SFINAE and therefore the warning is wrong.

include/etl/builder/expression_builder.hpp:345:1: note: V524 It is odd that the body of '>>=' function is fully equivalent to the body of '*=' function.

This one is interesting indeed. It is true that they are exactly because in ETL >> is used for scalar element-wise multiplication. This is quite interesting that PVS-Studio points that out. There was a few of these oddities but all were normal in the library.

etl/test/src/compare.cpp:23:1: error: V501 There are identical sub-expressions to the left and to the right of the '!=' operator: a != a

Again, it is nice that PVS-Studio finds that, but this is done on purpose on the tests to compare an object to itself. If I remove all the oddities in the test cases, there are only 17 left in the headers. None of the warnings on the test case was serious, but there was no more false positives either, so that's great.

include/etl/impl/vec/sum.hpp:92:1: error: V591 Non-void function should return a value.

template <typename L, cpp_disable_if((vec_enabled && all_vectorizable<vector_mode, L>::value))>
value_t<L> sum(const L& lhs, size_t first, size_t last) {
    cpp_unused(lhs);
    cpp_unused(first);
    cpp_unused(last);
    cpp_unreachable("vec::sum called with invalid parameters");
}

This one is interesting. It's not a false positive since indeed the function does not return a value, but there is a __builtin_unreachable() inside the function and it cannot be called. In my opinion, the static analyzer should be able to handle that, but this is really a corner case.

include/etl/sparse.hpp:148:1: note: V550 An odd precise comparison: a == 0.0. It's probably better to use a comparison with defined precision: fabs(A - B) < Epsilon.

inline bool is_zero(double a) {
    return a == 0.0;
}

This is not false, but again this is intended because of the comparison to zero for a sparse matrix. There were 10 of these in the same class.

include/etl/impl/blas/fft.hpp:562:1: note: V656 Variables 'a_padded', 'b_padded' are initialized through the call to the same function. It's probably an error or un-optimized code. Consider inspecting the 'etl::size(c)' expression. Check lines: 561, 562.

dyn_vector<etl::complex<type>> a_padded(etl::size(c));
dyn_vector<etl::complex<type>> b_padded(etl::size(c));

It's indeed constructed with the same size, but for me I don't think it's an odd pattern. I would not consider that as a warning, especially since it's a constructor and not a assignment.

include/etl/dyn_base.hpp:312:1: warning: V690 The 'dense_dyn_base' class implements a copy constructor, but lacks the '=' operator. It is dangerous to use such a class.

This is again a kind of corner case in the library because it's a base class and the assignment is different between the sub classes and not a real assignment in the C++ sense.

include/etl/impl/reduc/conv_multi.hpp:657:1: warning: V711 It is dangerous to create a local variable within a loop with a same name as a variable controlling this loop.

for (std::size_t c = 0; c < C; ++c) {
    for (std::size_t k = 0; k < K; ++k) {
        conv(k)(c) = conv_temp(c)(k);
    }
}

This is again a false positive... It really seems that PVS-Studio is not able to handle the operator().

include/etl/impl/pooling.hpp:396:1: error: V501 There are identical sub-expressions to the left and to the right of the '||' operator: P1 || P2 || P1

template <size_t C1, size_t C2, size_t C3,size_t S1, size_t S2, size_t S3, size_t P1, size_t P2, size_t P3, typename A, typename M>
static void apply(const A& sub, M&& m) {
    const size_t o1 = (etl::dim<0>(sub) - C1 + 2 * P1) / S1 + 1;
    const size_t o2 = (etl::dim<1>(sub) - C2 + 2 * P2) / S2 + 1;
    const size_t o3 = (etl::dim<2>(sub) - C3 + 2 * P3) / S3 + 1;

    if(P1 || P2 || P1){

Last but not least, this time, it's entirely true and it's in fact a bug in my code! The condition should be written like this:

if(P1 || P2 || P3){

This is now fixed in the master of ETL.

Conclusion

The installation was pretty easy, but the usage was not as easy as it could because the first method by analyzing the build system did not work. Fortunately, the system supports using the Clang compilation database directly and therefore it was possible to use.

Overall, it found 236 warnings on my code base (heavily templated library). Around 50 of them were in some of the extend libraries, but I forgot to filter them out. The quality of the results is pretty good in my opinion. It was able to find a bug in my implementation of pooling with padding. Unfortunately, there was quite a few false positives, due to SFINAE, bad handling of the operator() and no handling of __builtin_unreachable. The remaining were all correct, but were not bug considering their usages.

To conclude, I think it's a great static analyzer that is really fast compared to other one in the market. There are a few false positives, but it's really not bad compared to other tools and some of the messages are really great. An HTML report including the source code would be great as well.

If you want more information, you can consult the official site. There is even a way to use it on open-source code for free, but you have to add comments on top of each of your files.

I hope it was helpful ;)

Vector multiplication

The first kernel is a very simple one, simple element-wise multiplication of two vectors. Nothing fancy here.

For small vectors, clang is significantly slower than gcc-5.4 and gcc6.2. On vectors from 100'000 elements, the speed is comparable for each compiler, depending on the memory bandwidth. Overall, gcc-6.2 produces the fastest code here. clang-4.0 is slightly slower than clang-3.9, but nothing dramatic.

Vector exponentiation

The second kernel is computing the exponentials of each elements of a vector and storing them in another vector.

Interestingly, this time, clang versions are significantly faster for medium to large vectors, from 1000 elements and higher, by about 5%. There is no significant differences between the different versions of each compiler.

Matrix-Matrix Multiplication

The next kernel I did benchmark with the matrix-matrix multiplication operation. In that case, the kernel is hand-unrolled and vectorized.

There are few differences between the compilers. The first thing is that for some sizes such as 80x80 and 100x100, clang is significantly faster than GCC, by more than 10%. The other interesting fact is that for large matrices zapcc-clang-4.0 is always slower than clang-3.9 which is itself on par with the two GCC versions. In my opinion, it comes from a regression in clang trunk but it could also come from zapcc itself.

The results are much more interesting here! First, there is a huge regression in clang-4.0 (or in zapcc for that matter). Indeed, it is up to 6 times slower than clang-3.9. Moreover, the clang-3.9 is always significantly faster than gcc-6.2. Finally, there is a small improvement in gcc-6.2 compared to gcc 5.4.

Fast-Fourrier Transform

The following kernel is the performance of a hand-crafted Fast-Fourrier transform implementation.

On this benchmark, gcc-6.2 is the clear winner. It is significantly faster than clang-3.9 and clang-4.0. Moreover, gcc-6.2 is also faster than gcc-5.4. On the contrary, clang-4.0 is significantly slower than clang-3.9 except on one configuration (10000 elements).

1D Convolution

This kernel is about computing the 1D valid convolution of two vectors.

While clang-4.0 is faster than clang-3.9, it is still slightly slower than both gcc versions. On the GCC side, there is not a lot of difference except on the 1000x500 on which gcc-6.2 is 25% faster.

And here are the results with the naive implementation:

Again, on the naive version, clang is much faster than GCC on the naive, by about 65%. This is a really large speedup.

2D Convolution

This next kernel is computing the 2D valid convolution of two matrices

There is no clear difference between the compilers in this code. Every compiler here has up and down.

Let's look at the naive implementation of the 2D convolution (units are milliseconds here not microseconds):

This time the difference is very large! Indeed, clang versions are about 60% faster than the GCC versions! This is really impressive. Even though this does not comes close to the optimized. It seems the vectorizer of clang is much more efficient than the one from GCC.

4D Convolution

The final kernel that I'm testing is the batched 4D convolutions that is used a lot in Deep Learning. This is not really a 4D convolution, but a large number of 2D convolutions applied on 4D tensors.

Again, there are very small differences between each version. The best versions are the most recent versions of the compiler gcc-6.2 and clang-4.0 on a tie.

Conclusion

Overall, we can see two trends in these results. First, when working with highly-optimized code, the choice of compiler will not make a huge difference. On these kind of kernels, gcc-6.2 tend to perform faster than the other compilers, but only by a very slight margin, except in some cases. On the other hand, when working with naive implementations, clang versions really did perform much better than GCC. The clang compiled versions of the 1D and 2D convolutions are more than 60% faster than their GCC counter parts. This is really impressive. Overall, clang-4.0 seems to have several performance regressions, but since it's not still a work in progress, I would not be suprised if these regressions are not present in the final version. Since the clang-4.0 version is in fact the clang version used by zapcc, it's also possible that zapcc is introducing new performance regressions.

Overall, my advice would be to use GCC-6.2 (or 5.4) on hand-optimized kernels and clang when you have mostly naive implementations. However, keep in mind that at least for the example shown here, the naive version optimized by the compiler never comes close to the highly-optimized version.

As ever, takes this with a grain of salt, it's only been tested on one project and one machine, you may obtain very different results on other projects and on other processors.

Debug build

Let's start with the result of the debug build.

The results are almost the same as the previous test. zapcc is 3.3 times faster to compile than Clang and around 2 times faster than GCC. It seems that GCC 5.4 is a bit faster than GCC 4.9.3 while clang 3.9 is a bit slower than clang 3.7, but nothing terribly significant.

Overall, for debug builds, zapcc can bring a very significant improvement to your compile times.

Release build

Let's see what is the status of Release builds. Since the results are comparable between the numbers of threads, the results here are just for one thread.

This is more time consuming since a lot of optimizations are enabled and more features from ETL are enabled as well.

On a release build, the speedups are much less interesting. Nevertheless, they are still significant. zapcc is still 1.2 times faster than gcc and 1.5 times faster than clang. Then speedup against clang 3.9 is significantly higher than it was on my experiment with clang 3.7, it's possible that clang 3.9 is slower or simply has new optimization passes.

Conclusion

The previous conclusion still holds with modern version of compilers: zapcc is much faster than other compilers on Debug builds of template heavy code. More than 3 times faster than clang-3.9 and about 2 times faster than gcc-5.4. Since it's based on clang, there should not be any issue compiling projects that already compile with a recent clang. Even though the speedups are less interesting on a release build, it is still significantly, especially compared against clang.

I'm really interested in finding out what will be the pricing for zapcc once out of the beta or if they will be able to get even faster!

For the comparison with gcc 4.9.3 and clang 3.7, you can have a look at this article.

If you want more information about zapcc, you can go to the official website of zapcc

Debug build

Let's start with a debug build. In that configuration, there is no optimization going on and several of the features of the library (GPU, BLAS, ...) are disabled. This is the fastest way to compile ETL. I gathered this result on a 4 core, 8 threads, Intel processor, with an SSD.

The following table presents the results with different number of threads and the difference of zapcc compared to the other compilers:

The result is pretty clear! zapcc is around three times faster than Clang and around two times faster than GCC. This is pretty impressive!

For those that think than Clang is always faster than GCC, keep in mind that this is not the case for template-heavy code such as this library. In all my tests, Clang has always been slower and much memory hungrier than GCC on template-heavy C++ code. And sometimes the difference is very significant.

Interestingly, we can also see that going past the physical cores is not really interesting on this computer. On some computer, the speedups are interesting, but not on this one. Always benchmark!

Release build

We have seen the results on a debug build, let's now compare on something a bit more timely, a release build with all options of ETL enabled (GPU, BLAS, ...), which should make it significantly longer to compile.

Again, the table:

This time, we can see that the difference is much lower. Zapcc is between 1.2 and 1.4 times faster than Clang and between 1.1 and 1.3 times faster than GCC. This shows that most of the speedups from zapcc are in the front end of the compiler. This is not a lot but still significant over long builds, especially if you have few threads where the absolute difference would be higher.

We can also observe that Clang is now almost on par with GCC which shows that optimization is faster in Clang while front and backend is faster in gcc.

You also have to keep in mind that zapcc memory usage is higher than Clang because of all the caching. Moreover, the server are still up in between compilations, so this memory usage stays between builds, which may not be what you want.

As for runtime, I have not seen any significant difference in performance between the clang version and the zapcc. According to the official benchmarks and documentation, there should not be any difference in that between zapcc and the version of clang on which zapcc is based.

Incremental build

Normally, zapcc should shine at incremental building, but I was unable to show any speedup when changing a single without killing the zapcc servers. Maybe I did something wrong in my usage of zapcc.

Conclusion

In conclusion, we can see that zapcc is always faster than both GCC and Clang, on my template-heavy library. Moreover, on debug builds, it is much faster than any of the two compilers, being more than 2 times faster than GCC and more than 3 times faster than clang. This is really great. Moreover, I have not seen any issue with the tool so far, it can seamlessly replace Clang without problem.

It's a bit weird that you cannot allocate more than 2Go to the zapcc servers.

For a program, that's really impressive. I hope that they are continuing the good work and especially that this motivates other compilers to improve the speed of compilation (especially of templates).

If you want more information, you can go to the official website of zapcc

doctest

doctest is a framework quite similar to Catch but that claims to be much lighter. I tested doctest 1.0 early on, but at this point it was actually slower than Catch and especially slower than my versions of the macro.

Today, doctest 1.1 was released with promises of being even lighter than before and providing several new ways of speeding up compilation. If you want the results directly, you can take a look at the next section.

First of all, this new version improved the basic macros to make expression decomposition faster. When you use the standard REQUIRE macro, the expression is composed by using several template techniques and operator overloading. This is really slow to compile. By removing the need for this decomposition, the fast Catch macros are much faster to compile.

Moreover, doctest 1.1 also introduces CHECK_EQ that does not any expression decomposition. This is close to what I did in my macros expect that it is directly integrated into the framework and preserves all its features. It is also possible to bypass the expression checking code by using FAST_CHECK_EQ macro. In that case, the exceptions are not captured. Finally, a new configuration option is introduced (DOCTEST_CONFIG_SUPER_FAST_ASSERTS) that removes some features related to automatic debugger breaks. Since I don't use the debugger features and I don't need to capture exception everywhere (it's sufficient for me that the test fails completely if an exception is thrown), I'm more than eager to use these new features.

Results

For evaluation, I have compiled the complete test suite of ETL, with 1 thread, using gcc 4.9.3 with various different options, starting from Catch to doctest 1.1 with all compilation time features. Here are the results, in seconds:

As you can see, doctest 1.1 is much faster to compile than doctest 1.0! This is really great news. Moreover, it is already 16% faster than Catch. When all the features are used, doctest is 12% faster than my stripped down versions of Catch macros (and 43% faster than Catch standard macros). This is really cool! It means that I don't have to do any change in the code (no need to strip macros myself) and I can gain a lot of compilation time compared to the bare Catch framework.

I really think the author of doctest did a great job with the new version. Although this was not of as much interest for me, there are also a lot of other changes in the new version. You can consult the changelog if you want more information.

Conclusion

Overall, doctest 1.1 is much faster to compile than doctest 1.0. Moreover, it offers very fast macros for test assertions that are much faster to compile than Catch versions and even faster than the versions I created myself to reduce compilation time. I really thing this is a great advance for doctest. When compiling with all the optimizations, doctest 1.1 saves me 50 seconds in compilation time compared to the fast version of Catch macro and more than 5 minutes compared to the standard version of Catch macros.

I'll probably start using doctest on my development machine. For now, I'll keep Catch as well since I need it to generate the unit test reports in XML format for Sonarqube. Once this feature appears in doctest, I'll probably drop Catch from ETL and DLL

If you need blazing fast compilation times for your unit tests, doctest 1.1 is probably the way to go.

Usage

The usage is pretty simple. You put the coverage executables in your PATH variable and activate coverage globally. Then, we you compile, the compiler calls will be intercepted and a coverage file will be generated. When the compilation is done, run the program and the coverage measurements will be filled.

The coverage results can then be exported to HTML (or XML) or visualized using the CoverageBrowser tool:

It's a pretty good view of the coverage result. You have a breakdown by folders, by file, by function and finally by condition. You can view directly the source code:

If you want to exclude some code from your coverage reports, you can use a pragma:

switch (n) {
    case 1: one++; break;
    case 2: two++; break;
    case 3: three++; break;
    #pragma BullseyeCoverage off
    default: abort();
    #pragma BullseyeCoverage on
}

So that the condition won't be set as uncovered.

As for the coverage, it's pretty straightforward. For example:

covmerge -c -ffinal.cov sse.cov avx.cov

and it's really fast. Unfortunately, the merging is only done at the function level, not at the statement or at the condition level. This is a bit disappointing, especially from a commercial tool. Nevertheless, it works well.

Conclusion

To conclude, Bullseye seems to be a pretty good tool. It has more features than standard gcov coverage and all features are well integrated together. I have only covered the features I was interested in, there are plenty of other things you can look at on the official website.

However, if you don't need the extra features such as the visualizer (or use something like Sonar for this), or the merge or code excluding, it's probably not worth paying the price for it. In my case, since the merge is not better than my C++ tool (both do almost the same and my tool does some basic line coverage merging as well) and I don't need the visualizer, I won't pay the price for it. Moreover, they don't have student or open source licensing, therefore, I'll continue with my complicated toolchain :)

Examples

Here is an example of expressions in ETL:

etl::fast_matrix<float, 2, 2, 2> a = {1.1, 2.0, 5.0, 1.0, 1.1, 2.0, 5.0, 1.0};
etl::fast_matrix<float, 2, 2, 2> b = {2.5, -3.0, 4.0, 1.0, 2.5, -3.0, 4.0, 1.0};
etl::fast_matrix<float, 2, 2, 2> c = {2.2, 3.0, 3.5, 1.0, 2.2, 3.0, 3.5, 1.0};

etl::fast_matrix<float, 2, 2, 2> d(2.5 * ((a >> b) / (log(a) >> abs(c))) / (1.5 * scale(a, sign(b)) / c) + 2.111 / log(c));

Or another I'm using in my neural networks library:

h = etl::sigmoid(b + v * w)

In that case, the vector-matrix multiplication will be executed using a BLAS kernel (if ETL is configured correclty) and the assignment, the sigmoid and the addition will be automatically vectorized to use either AVX or SSE depending on the machine.

Or with a convolutional layer and a ReLU activation function:

etl::reshape<1, K, NH1, NH2>(h_a) = etl::conv_4d_valid_flipped(etl::reshape<1, NC, NV1, NV2>(v_a), w);
h = max(b_rep + h_a, 0.0);

This will automatically be computed either with NVIDIA CUDNN (if available) or with optimized SSE/AVX kernels.

For more information, you can take a look at the Reference on the wiki.

Next version

For the next version, I'll focus on several things:

• Improve matrix-matrix multiplication kernels when BLAS is not available. There is a lot of room for improvement here

• Complete support for symmetric matrices (currently experimental)

• Maybe some new adapters such as Hermitian matrices

• GPU improvements for some operations that can be done entirely on GPU

• New convolution performanceimprovements

• Perhaps more complete parallel support for some implementations

• Drop some compiler support to use full C++14 support

Download ETL

You can download ETL on Github. If you only interested in the 1.0 version, you can look at the Releases pages or clone the tag 1.0. There are several branches:

• master Is the eternal development branch, may not always be stable

• stable Is a branch always pointing to the last tag, no development here

For the future release, there always will tags pointing to the corresponding commits. I'm not following the git flow way, I'd rather try to have a more linear history with one eternal development branch, rather than an useless develop branch or a load of other branches for releases.

Don't hesitate to comment this post if you have any comment on this library or any question. You can also open an Issue on Github if you have a problem using this library or propose a Pull Request if you have any contribution you'd like to make to the library.

Hope this may be useful to some of you :)

Sensors

asgard support several sensors:

• DHT11 Temperature/Humdity Sensor

• WT450 Temperature/Humdity Sensor

• RF Button

• IR Remote

• CPU Temperature Sensor

You can see the sensors data displayed on the web interface:

Actions

There are currently a few actions provided by the drivers:

• Wake-On-Lan a computer by its MAC Address

• ITT-1500 smart plugs ON and OFF

• Kodi actions: Pause / Play / Next / Previous on Kodi

Here are the rules engine:

My home automation

I'm currently using this system to monitor the temperature in my appartment. Nothing great so far because I don't have enough sensors yet. And now, I'm also using a wireless button to turn on my power socket, wait 2 seconds and then power on my Kodi Home Theater with wake on lan.

It's nothing fancy so far, but it's already better than what I had with Ninja Blocks, except for the ugly hardware ;).

Future

There are still tons of work on the project and on integration in my home.

• I'm really dissatisfied with the WT450 sensor, I've ordered new Oregon sensors to try to do better.

• I've ordered a few new sensors: Door intrusion detector and motion detector

• The rules system needs to be improve to support multiple conditions

• I plan to add a simple state system to the asgard server

• There are a lot of refactorings necessary in the code and

However, I don't know when I'll work on this again, my work on this project is pretty episodic to say the least.

Code

The code is, as always, available on Github. There are multiple repositories: all asgard repositories. It's not that much code for now, about 2000 lines of code, but some of it may be useful. If you plan to use the system, keep in mind that it was never tested out of my environment and that there is no documentation so far, but don't hesitate to open Issues on Github if you have questions or post a comment here.

Thor

My project Thor Operating System now has much more features than before:

• 64bit operating system

• Preemptive Multiprocessing

• Keyboard / Mouse driver

• Full ACPI support with ACPICA

• Read/Write ATA driver

• FAT32 file system support

• HPET/RTC/PIT drivers

• Basic PCI support

• Multi stage booting with FAT32

Since last time, I've fixed tons of bug in the system. Although there are still some culprit, it's much more stable than before. They were a lot of bugs in the scheduler with loads of race conditions. I hope I've working through most of them now.

I'm currently working on the network stack. I'm able to receive and send packets using the Realtek 8139 card. I have working support for Ethernet, IP and ARP. I'm currently working on adding ICMP support. I've come to realize that the hardest part is not to develop the code here but to find a way to test it. Network in Qemu is a huge pain in the ass to configure. And then, you need tools to generate some packets or at least answer to packets send by the virtual machine, and it's really bad... Nevertheless, it's pretty fun overall :)

Aside from this, I'm also working on a window manager. I'll try to post an update on this.

You can take a look at the thor sources if you're interested.

Future

For the time being, I'll focus my effort on the thor project. I also have some development to do on my home automation system: asgard-server that I plan to finalize and deploy in a useful way this weekend in my apartment. You can also expect some updates on my deep learning library where I've started work to make it more user-friendly (kind of). I'm also still waiting on the first stable version of doctest for a new comparison with Catch.

I really want to try to publish again some more posts on the blog. I'll especially try to publish some more updates about Thor.