Assimp 4.1.0 Windows x64 VS 2017 Debug/Release binaries

I’ve built Assimp 4.1.0 Windows x64 release artifacts (lib/dlls), using VS 2017 (should work for VS 2015 but untested) and put up the libraries.

It is available at – https://github.com/tharindu-mathew/assimp/releases/tag/v4.1.0-Win-x64

Posted on December 12, 2018December 12, 2018 by Caludewa Tharindu Mathew 0

Building Caffe on Windows with CUDA 9.0 for VS 2013, 2015 and 2017

Building Caffe on Windows 10 has been a journey (to put it lightly). Since I have a new gen gfx card (new for 2018), it does not support CUDA 8.0. So, all posts saying you need CUDA 8.0 are outdated (at least to me). With CUDA 9.0, Windows caffe does not compile.

The good news after a couple of days of trying I’ve figured out a workaround. The only boost version that supports CUDA 9.0 as of now is boost 1.65.1 and above. But, interestingly cmake breaks with boost 1.66.0. I know, welcome to the real-world versioning hell when it comes to actually building stuff.

So, if you got your windows source from: https://github.com/BVLC/caffe/tree/windows

You need to do a couple of stuff. First download and install boost 1.65.1 in some path. Let’s call this root directory my_boost_1_65_1 (typically C:\local\boost_1_65_1), and the library directory (which changes based upon which VS version you downloaded, typically C:\local\boost_1_65_1\lib64-msvc-14.0 for VS 2015). Yes, it sucks that MSVC version is 14.0 for VS 2015, but such is the life living in a Microsoft world.

I assume you have checked out the 1.0 version of windows caffe. Now, open the build_windows.cmd in the scripts directory, and modify the cmake command as follows (note the 3 lines referring to boost and boost paths):
cmake -G"!CMAKE_GENERATOR!" ^ -DBLAS=Open ^ -DCMAKE_BUILD_TYPE:STRING=%CMAKE_CONFIG% ^ -DBUILD_SHARED_LIBS:BOOL=%CMAKE_BUILD_SHARED_LIBS% ^ -DBUILD_python:BOOL=%BUILD_PYTHON% ^ -DBUILD_python_layer:BOOL=%BUILD_PYTHON_LAYER% ^ -DBUILD_matlab:BOOL=%BUILD_MATLAB% ^ -DCPU_ONLY:BOOL=%CPU_ONLY% ^ -DCOPY_PREREQUISITES:BOOL=1 ^ -DINSTALL_PREREQUISITES:BOOL=1 ^ -DUSE_NCCL:BOOL=!USE_NCCL! ^ -DCUDA_ARCH_NAME:STRING=%CUDA_ARCH_NAME% ^ -DBOOST_NO_BOOST_CMAKE=TRUE ^ -DBOOST_ROOT=C:/boost_1_65_1 ^ -DBOOST_LIBRARYDIR=C:/boost_1_65_1/lib64-msvc-14.0 ^ "%~dp0\.."

I do the following edit as well, so I know that boost version is correct or it will fail. So, edit the boost version in Dependencies.cmake (located in the cmake directory):

# ---[ Boost find_package(Boost 1.65 REQUIRED COMPONENTS system thread filesystem) list(APPEND Caffe_INCLUDE_DIRS PUBLIC ${Boost_INCLUDE_DIRS}) list(APPEND Caffe_DEFINITIONS PUBLIC -DBOOST_ALL_NO_LIB) list(APPEND Caffe_LINKER_LIBS PUBLIC ${Boost_LIBRARIES})

And, by changing these two files, everything should be good. Now, execute scripts\build-windows.cmd and watch your build succeed.

l_prerequisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-heap-l1-1-0.dll' [D:\tools\caffe\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-heap-l1-1-0.dll' returning type 'system' -- possibly incorrect [D:\tools\caffe\build\tools\upgrade_solver_proto_text_install_ prerequisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-stdio-l1-1-0.dll' [D:\tools\caffe\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-stdio-l1-1-0.dll' returning type 'system' -- possibly incorrect [D:\tools\caffe\build\tools\upgrade_solver_proto_text_install _prerequisites.vcxproj] CUSTOMBUILD : warning : cannot resolve item 'api-ms-win-crt-convert-l1-1-0.dll' [D:\tools\caffe\build\tools\upgrade_solver_proto_text_install_prerequisites.vcxproj] CUSTOMBUILD : -- warning : gp_resolved_file_type non-absolute file 'api-ms-win-crt-convert-l1-1-0.dll' returning type 'system' -- possibly incorrect [D:\tools\caffe\build\tools\upgrade_solver_proto_text_insta ll_prerequisites.vcxproj]


6897 Warning(s)

0 Error(s)

Time Elapsed 00:14:44.49
Let me know in the comments if you run into more issues.

Posted on April 13, 2018April 16, 2018 by Caludewa Tharindu Mathew 17

Debugging CUDA – Tips and Tricks

CUDA is fast but painful to debug. It’s similar to working with openGL, which gives brilliant results when it works, but you have no idea what’s going on when it doesn’t. I’m listing down a number of ways that you can use to track down issues in your CUDA algorithms. Hopefully, it will ease the pain that I had to go through.

Install Nsight and use CUDA Debugging

This step seems rather obvious, and Nsight gets installed when you install CUDA. But, surprisingly its not obvious to a beginner how to use and why you should use it. If you are using Visual Studio, and are having problems with your CUDA algorithm, follow these steps to start debugging. Make sure the project is built in “Debug” mode. After building it (don’t run it), open the Nsight menu and click CUDA Debugging. And now, you should be able to conveniently place breakpoints within your CUDA kernels, that get hit. Also, look at the Nsight output in your output information, and watch out for error codes.

CUDA Memory checking

Always, make sure for memory access violations. Click on the Nsight menu and make sure “Enable CUDA Memory checker” is checked and follow the steps under point 1 to debug your application. If there are memory access violations stop right there! This is the first thing you should correct. Even if your algorithm runs and you are getting some results, there can be plenty of subtle bugs lying around when memory access violations happen. A common error that happens is because some threads access your arrays outside their index. So you need to block proceeding if a thread index is outside by including a return statement after an index range check like below:

int x_index = blockDim.x * blockIdx.x + threadIdx.x;
int y_index = blockDim.y * blockIdx.y + threadIdx.y;
 
if ((x_index &gt;= cols) 
	|| (y_index &gt;= rows)) {
	return;
}

Understand Nsight debugging output

Make yourself familiar with the CUDA runtime error codes. Nsight will sometimes give output with an error such as “Program hit error 9 on execution”. Now, what you have to do is look up this error code with the documentation that you are using. Let’s look it up here – http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html#group__CUDART__TYPES_1g3f51e3575c2178246db0a94a430e0038. Aha! now we know what error 9 means. It says “This indicates that a kernel launch is requesting resources that can never be satisfied by the current device. Requesting more shared memory per block than the device supports will trigger this error, as will requesting too many threads or blocks. See cudaDeviceProp for more device limitations.” We probably asked the kernel to use 100000 threads per block or something to that effect, which is out of the limit of threads that the device can use per block. Now, we know we need to check what the values we are passing and adjust that.

Time your functions

This is something that I found extremely helpful. Here’s a simple C++ snippet I use:

Clock::time_point t0 = Clock::now();
CUDA_segment(pre_segmentation_img, post_segmentation_img, vis_img);
Clock::time_point t1 = Clock::now();
milliseconds ms = std::chrono::duration_cast&lt;milliseconds&gt;(t1 - t0);
std::cout &lt;&lt; "Time taken for segmentation: " &lt;&lt; ms.count() &lt;&lt; "ms\n";

In addition to telling your execution time, which probably matters to you since you are trying to use CUDA, it also tells you if your CUDA execution failed. If you are getting a run time like 1ms for something that would usually take about 500ms, you need to hold your enthusiasm. Your algorithm didn’t suddenly become super fast. Your CUDA code probably ran into an error, and exited.

Use a single thread and a single block and check sequential execution logic

If there is a problem with your algorithm and you need to understand why it’s failing, try simplifying your kernel execution to a single thread. This allow you to forget the complexity of parallel execution and debug it like a single threaded application. Just use block size = 1, and threads per block = 1. Also, do any additional modifications to your kernel code so that it goes on the same path every time you debug, i.e. if your processing an image, make sure it operates on the same sequences of pixels, by hard coding the x and y indices (x_index = 200, y_index = 200).

convert_2_closest_color &lt;&lt;&lt;1, 1&gt;&gt;&gt; (cuda_img, valid_colors_);

Fast debugging – Use printf

After following step 3, I prefer to use a lot of printfs for debugging. This allows me to execute the code in “Release” mode, and see what exactly is going wrong at a fast execution speed.

NOTE: Make sure you disable all printfs through a macro when you want to use this code in production

Write back your output to files and check your output

Even with debugging, the data structures you use are hard to check because of the massive parallelism that’s inherent with CUDA. Try to write out the effects of the intermediate steps of your algorithm by doing a cudaMemCpy from device to host. I usually write out the data into CSV files or image files and check the output for any issues that I can see. If you can visualize the data, you will notice a lot of issues that can result due to errors in your code.

I hope this helped to ease some of the pain that you are suffering due to programming CUDA. Don’t get me wrong I love CUDA, and I truly love the end execution times it gives for my algorithms. But debugging is quite a process and needs to get used to 🙂

Posted on July 9, 2015July 21, 2015 by Caludewa Tharindu Mathew 0

Don’t use memset for initializing floats or doubles

I’ve been dabbling at a bit of C for some extremely optimized code, and it has served me well. I’ve also learnt some lessons. Here is one lesson I learnt regarding memset.

I had a large array of floats.


int great_array_size = 100000;

float * the_great_array = (float*) malloc (sizeof(float) * great_array_size));

Now, I wanted a fast way of initializing the array to a specific value. So, without thinking too much I used memset. I did not have any knowledge when I was using this that it was mainly used to initialize strings.


float great_initial_value = 0.005f;
memset(the_great_array, great_initial_value, great_array_size);

Instead of fast initialization what I got was a world of hurt. Memset will convert the passed value to a char value, and in this case use the least significant byte value of the float and use this value to initialize your array.

The correct way to initialize a float array is the obvious way.


for (size_t i = 0; i < great_array_size;++i) {
    the_great_array[i] = great_initial_value;
}

Sigh. It seems easy now that I know what happens. Oh, well.

Posted on June 30, 2015October 12, 2018 by Caludewa Tharindu Mathew 2

The difference between malloc and new

The difference between malloc and new is subtle, but important if you are mixing C and C++. malloc will allocate the memory needed for your object. new will allocate your memory and call your constructor as well, executing any code in it.

The same difference applies to free and delete.

Here’s a code example.

#include <cstdlib>
#include <iostream>

struct MyClass {
    
int property = 0;
 
MyClass() {
    property = 10;
}

~MyClass() {
    std::cout << "Object destructor called" << std::endl;
}
 
};
 
int main(int argc, char** argv) {
   MyClass *my_class_malloc = (MyClass*) malloc(sizeof(MyClass)); // just allocated memory
   std::cout << "Property : " << my_class_malloc->property << std::endl;
   MyClass *my_class_new = new MyClass(); // calls constructor and sets to 10
   std::cout << "Property : " << my_class_new->property << std::endl;
   
   std::cout << "Calling free..." << std::endl;
   free(my_class_malloc);
   
   std::cout << "Calling delete..." << std::endl;
   delete(my_class_new);
}

Posted on April 15, 2015 by Caludewa Tharindu Mathew 0

List of must have plugins for coding C++ in Visual Studio

On this post I maintain a list of plugins I use with Visual Studio.

Power Tools

If you are used to control+click through Eclipse or Idea usage. You will love this set of tools. It has tons of useful tweaks that makes Visual Studio much easier to use.

Available at: https://visualstudiogallery.msdn.microsoft.com/dbcb8670-889e-4a54-a226-a48a15e4cace

VsVim

I’m a big vim fan for ease of navigation, fast editing and movement. If you are this will be a life saver for you, as you will never have to use arrow keys or the mouse for navigation.

Available at: https://visualstudiogallery.msdn.microsoft.com/59ca71b3-a4a3-46ca-8fe1-0e90e3f79329

Refactoring (Only rename)

This feature is almost expected of IDEs and is primarily one of the reasons why we use them for complex projects. Unfortunately, this plugin only has renaming as the supported refactoring option. I really miss the extract method that was available in other IDEs after I switched to Visual C++ for my work. Anyway, it’s better than nothing.

Available at: https://visualstudiogallery.msdn.microsoft.com/164904b2-3b47-417f-9b6b-fdd35757d194

4. Image Watch (as mentioned by Chris May)

This is a really cool plugin if you are working with openCV. It helps you get rid of the std::cout statements you need to include to see the contents of matrices, and makes working with images a pleasure rather than a pain.

Available at: https://visualstudiogallery.msdn.microsoft.com/e682d542-7ef3-402c-b857-bbfba714f78d

Let me know if there are any other plugins that you find useful for coding C++ in Visual Studio.

Posted on March 20, 2015March 26, 2015 by Caludewa Tharindu Mathew 2

Making Visual Studio Intellisense work with QT

Disclaimer: This solution worked for Visual Studio 2013 with Qt 5.3

One of the reasons if your Qt program runs fine but the Visual Studio Intellisense does not detect Qt specific variables can be due to the necessary header files not being included. You will notice if you go to Project Properties->C/C++->Additional Include Directories, that there are several include entries defined with using QTDIR as an environment variable.

I’m sure you have figured out the solution now. You have to define this system variable to make Intellisense work. Add a new system variable called QTDIR, and point it to the QT installation with the version you are working with, ex: C:\Qt\5.3\msvc2012_opengl. This should solve your problem.

Note: There seems to be other problems that can cause Intellisense to not work with Qt, and they are pointed out in: http://qt-project.org/faq/answer/intellisense_does_not_work_for_my_qt_application._whats_wrong

Posted on February 7, 2015 by Caludewa Tharindu Mathew 1

Solving the Error 127 from Make when cross compiling to raspberry pi

I was following the excellent tutorial from Alessio on cross compiling to raspberry pi from Windows 7. Then, I hit the the dreaded ‘Error 127’ from make. Now, after hours of searching I couldn’t find how to solve this. Then, Bryan has mentioned that you need to install the 32 bit cygwin version and that would work, and works it does.

If you already installed cygwin 64 bit version like me and wondering how to install it,here are some steps:

download the 32 bit setup to a new directory(ex: C:\cygwin32) and run the setup. Make sure you make every path point to this new directory during the setup process.
Now, copy the crosstools directory to this new directory. So, it will be at C:\cygwin32\opt\cross\x-tools\arm-unknown-linux-gnueabi
Change all paths created in Eclipse in the ‘Path’ variable (and Windows Environment variables too, if you added cygwin there) to point to this new location
Now, clean and build the project and it should compile for you!

Another error I ran across is this:

Could not determine GDB version after sending: C:\cygwin32\opt\cross\x-tools\arm-unknown-linux-gnueabi\bin\arm-unknown-linux-gnueabi-gdb.exe –version

If you ran across this you installed python 2.7 instead of 2.6. Re-run the cygwin setup and change the python version to 2.6.x.x and install this python version. Re-run your program and it should be working.

I wrote this so it helps someone else to save a ton of time. HTH.

Posted on February 13, 2014February 13, 2014 by Caludewa Tharindu Mathew 0

Best Practices in the WSO2 Carbon Platform

This post discusses best practices when programming with the WSO2 Carbon platform, which is the base for all WSO2 products.

Here are the main points discussed in this post:

Do not hardcode compile time, run time dependencies and re-use properties in the root pom.
Use OSGI Declarative Services
Do not copy paste code, re-use code through OSGI, Util methods, etc.
Understand Multi tenancy and design for Multi Tenancy
Write tests for your code

These points are discussed in detail below giving reasons and a HOWTO for each point. Hope you find the details useful as this is a long (and probably boring) read.

Do not hardcode compile time, run time dependencies and re-use properties in the root pom.

Ex:

<version>1.0.0.wso2v1</version>

</dependency>

Why do this?

This should be avoided as it threatens the stability of the build. If two versions of the same jar comes into a product, it can cause OSGI related errors, that can take some time to identify and fix.

How to avoid this?

Make sure the root pom (components/pom.xml or platform/pom.xml) has a dependency version defined, and use that.

Ex: in platform/pom.xml

<orbit.version.json>2.0.0.wso2v1</orbit.version.json>

If it is not defined, please define it in the root pom and use this version.

2. Use OSGI declarative services

Do not get service references in the activate method.

ServiceReference serviceReference = componentContext.getBundleContext().getServiceReference(Foo.class.getName());

if(serviceReference != null){

Foo = (Foo) componentContext.getBundleContext().getService(serviceReference);

}

Why do this?

(Quoting, Pradeep here) This can lead to erroneous situations and you will have to check whether services are available in a while loop to make it work properly. And it becomes complicated when two or more service references.

Why bother to do all this when the DS framework handles all this for you.

How to avoid this?

Use a DS reference in your service component.

Ex:

* @scr.reference name=”foo.comp”

* interface=”org.wso2.carbon.component.Foo”

* cardinality=”1..1″ policy=”dynamic” bind=”setFoo” unbind=”unsetFoo”

protected void setFoo(Foo foo) {

// do whatever ex:

manager.setFoo(foo);

}

protected void unsetFoo(Foo foo) {

// make sure lose the reference

manager.setFoo(null);

}

3. Do not copy paste code, re-use code through OSGI, Util methods, etc.

If you need functionality provided by other components, don’t copy paste code. Find another way to re-use the code. If you need some common thing done most probably, there exists a util method to that, or an osgi method. If not add or create one.

Why do this?

It might take some effort and discipline but later on you (or someone else) will have to write less code. If changes happen to the original code, you will have to fix the copy pasted code as well (which is often missed).

How to do this?

i. Use util methods/ constants

Easily said with an example. Ex: To split domain name from user name use, MultitenantUtils.getTenantDomain(username);

Same applies for constants. Ex: MultitenantConstants.SUPER_TENANT_DOMAIN

ii. Use and expose OSGI services

You can simply expose any class you want by registering an OSGI service,

ex: In the bundle activator,

context.getBundleContext().

registerService(Foo.class.getName(), new Foo(), null);

Get a reference and re-use as pointed in point 2.

4. Understand Multi tenancy and design for Multi Tenancy

I feel that some folks don’t understand what multi tenancy (MT) means. It is an important aspect of the platform and it should not be an after thought, but a part of the design.

Why do this?

Making code work for multiple tenants needs some careful design. It may not be straight forward for some cases. So thinking about it after a release or when you want to make the code work for MT may require some heavy refactoring. Now with the products and services merged, multi tenancy should not be separate at all.

How to do this?

This is an extensive topic so I will not go into details.

Using AxisConfigurationContextObserver, Tenant aware registries are some easy ways provided by the platform. If you are depending on a non-MT dependency, you will have to figure out how to make it work in the MT case. You can always get help from other folks who have done MT aware stuff.

5. Write tests for your code

Make sure you write tests for your code and gain a good % of code coverage. Folks will not know whether changes will break functionality or not until it is too late.

Why do this?

The reasons are obvious and have been stated by many. But to re-iterate, this makes the code base extremely stable. Other folks can change your code to fix bugs or do enhancements without worrying about breaking functionality or actually breaking functionality.

How to do this?

I personally prefer unit tests. But we have an integration test framework and as well as a system test framework (Clarity). Make sure you have tests to address to cover most functionality, if not all functionality. Features should not be considered complete, without test coverage.

If you find improvements on the points spoken, please do leave a comment and I will incorporate it into the post.

Posted on October 17, 2012 by Caludewa Tharindu Mathew 0

Shortcuts for Ajax calls in Jquery

This post should help a lot of people starting in jQuery and javascript. For ease of reference, I have borrowed code present in the ajax jquery docs.

The standard way in jQuery to make an ajax call is by using,

$.ajax({
  type: 'POST',
  url: url,
  data: data,
  success: success,
  dataType: dataType
});

This gives you an infinite number of tweaks that’s available (all documented here). But, the truth is 90% of the time you do not need these extra options. So, here are a few shortcut functions that allows us to write even less with jQuery.

$.post

Let’s take an example use of $.post:

$.post('bam_data_processor.jsp', 'processAll=true', function(data) {
  $('#hadoop_result').html(data);
});

This is shorthand for,

$.ajax({
  type: 'POST',
  url: 'bam_data_processor.jsp',
  data: 'processAll=true',
  success: function(data) {
  $('#hadoop_result').html(data);
}),
});

$.get

Now, let’s take an example use of $.get:

$.get('bam_stored_configs.jsp', function(data) {
  $('#hadoop_result').html(data);
}, "json");

$.get is shorthand for

$.ajax({
  url: 'bam_data_processor.jsp',
  success: function(data) {
    $('#hadoop_result').html(data);
  }),
  dataType : "json"
});

$.getJSON

An even shorter method for dealing with JSON is.

$.getJSON('bam_stored_configs.jsp', function(data) {
  $('#hadoop_result').html(data);
});

which is shorthand for:

$.get('bam_stored_configs.jsp', function(data) {
  $('#hadoop_result').html(data);
}, "json");

It’s really great that the jQuery devs introduce conveniences like these to write even less code. HTH.

Posted on July 3, 2012 by Caludewa Tharindu Mathew 1

Caluadewa Tharindu Mathew's Website

Computer Graphics Programming, Computer Vision, AI and Machine Learning

Caluadewa Tharindu Mathew's Website

Category: programming

Assimp 4.1.0 Windows x64 VS 2017 Debug/Release binaries

Building Caffe on Windows with CUDA 9.0 for VS 2013, 2015 and 2017

Debugging CUDA – Tips and Tricks

Don’t use memset for initializing floats or doubles

The difference between malloc and new

List of must have plugins for coding C++ in Visual Studio

Making Visual Studio Intellisense work with QT

Solving the Error 127 from Make when cross compiling to raspberry pi

Best Practices in the WSO2 Carbon Platform

Shortcuts for Ajax calls in Jquery

$.post

$.get

$.getJSON