Cuda Clion

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Sep 10, 2020 I have simply downloaded the OptiX SDK (7.1), and have obviously installed CUDA (11). When opening a sample project in CLion, everything runs as it should, however, all the optix libraries are unknown to the IDE. I am having trouble with build CUDA project in CLion. I am having CLion 2020.1.2 and CUDA 11 installed on Windows 10 2004. After I create A simple CUDA project. I got following error: With MinGW x6. CLion CUDA Run Patcher. Compatible with CLion. Ratings & Reviews. Hello there, time to upgrade to the latest Clion? Fixed a few minor things and got it to work in CLion 2018.2.1. Palani to accept the merge and publish the updated version. CUDA C and C are essentially C/C with a few extensions, and CLion 2020.1 is now able to handle CUDA code correctly. Can not open a CUDA project in the CLion IDE - Stack Overflow stackoverflow.com.

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  • Installation
  • API Overview
  • API Details
  • Examples

Titan is a versatile CUDA-based physics simulation library that provides a GPU-accelerated environment for physics primatives like springs and masses. Library users can create masses, springs, and more complicated objects, apply constraints, and modify simulation parameters in real time, while the simulation runs asynchronously on the GPU. Titan has been used for GPU-accelerated reinforcement learning, physics and biomedical research, and topology optimization.

#include <Titan/sim.h>
int main() {
sim.createLattice(titan::Vec(0, 0, 10), titan::Vec(5, 5, 5), 5, 5, 5); // create lattice with center at (0, 0, 10) and given dimensions
sim.createPlane(titan::Vec(0, 0, 1), 0); // create constraint plane
}

For more examples and troubleshooting, see the github wiki. We also have a user Google Group where you can ask questions about installation and usage, and make feature requests.

Also see this overview video for an overview of the library and its capabilities.

Requirements

Windows

  • Microsoft Visual Studio 2015 or 2017
  • Nvidia CUDA Toolkit
  • vcpkg for installing dependencies

Linux

  • gcc compiler
  • Nvidia CUDA Toolkit

Linux Installation

Linux Quick Installation:

TLDR (note this requires CUDA to be installed):

./clean-build.sh debug # (or release)

The clean-build.sh script can also take CMake flags as arguments. For example, to build tests, do

./clean-build.sh debug --DTITAN_BUILD_TESTS=ON

or to build with support for Verlet integration do

./clean-build.sh debug --DTITAN_BUILD_TESTS=ON -DVERLET=ON

Other options include -DGRAPHICS, -DCONSTRAINTS, -DRK2. See the CMakeFile for full documentation.

If this doesn't work, try the following manual installation steps:

1. Install the NVIDIA CUDA Toolkit

Download the NVIDIA CUDA Toolkit from this link and follow the quick install instructions. If the installation fails, try again using the advanced installation tab after unchecking Visual Studio Integration. This is a known CUDA big caused by incompatibilities with some Visual Studio versions.

2. Install vcpkg

We will be using the vcpkg utility to handle dependencies for this project. The library optionally supports graphics rendering of the mass spring simulation using OpenGL, with utilities provided by the GLM, GLEW, and GLFW libraries. These libraries can be installed in any fashion, but the Microsoft vcpkg package manager provides a convenient method. To use vcpkg,

  1. Go to the vcpkg GitHub and clone the repository into your user account (ideally in /home/username/Documents or /home/username/Downloads) using the following:
git clone https://github.com/Microsoft/vcpkg.git
  1. Follow the installation/setup instructions provided in the GitHub (reproduced here) to build vcpkg
./bootstrap-vcpkg.sh

The last command will output a path to the vcpkg cmake file which you will need to include in future projects to use the Titan library. Save this output, for example: '-DCMAKE_TOOLCHAIN_FILE=/home/username/Documents/vcpkg/scripts/buildsystems/vcpkg.cmake'

3. Download and Install Titan

To install the library using vcpkg, clone the github repository to a folder on your computer using

git clone https://github.com/jacobaustin123/Titan.git

Inside the newly downloaded Titan directory, navigate to Titan/vcpkg and copy the 'titan' directory there to the ports folder in the vcpkg installation folder from step 2. For example, if vcpkg was installed in C:/vcpkg, run:

cp -r titan ~/Documents/vcpkg/ports/

Then in the vcpkg installation folder, run

which will handle all of the dependencies for you. If vcpkg fails to find CUDA, try running export CUDA_PATH=/usr/local/cuda, or whatever the path is to your CUDA installation. You can copy that line into your .bashrc file to avoid having to run it every time. At the moment, due to an issue with CUDA and CMake, you will need to include the line

at the beginning of whatever project uses the Titan library, with the myproject variable replaced by the name of your project. This is because certain environment variables which are needed by the library are not being set properly. In some cases, if CMake cannot find CUDA, you will need to manually set

or whatever the path to the CUDA nvcc compiler is.

Windows Installation:

This quick installation guide assumes you already have a C++ compiler installed, like Microsoft Visual Studio 2015/2017. We will:

  • Install the NVIDIA CUDA Toolkit
  • Install the Microsoft vcpkg package manager
  • Build and install Titan and its dependencies

1. Install the NVIDIA CUDA Toolkit

Download the NVIDIA CUDA Toolkit from this link and follow the quick install instructions. If the installation fails, try again using the advanced installation tab after unchecking Visual Studio Integration. This is a known CUDA big caused by incompatibilities with some Visual Studio versions.

2. Install vcpkg

We will be using the vcpkg utility to handle dependencies for this project. The library optionally supports graphics rendering of the mass spring simulation using OpenGL, with utilities provided by the GLM, GLEW, and GLFW libraries. These libraries can be installed in any fashion, but the Microsoft vcpkg package manager provides a convenient method. To use vcpkg,

  1. Go to the vcpkg GitHub and clone the repository into your user account (ideally in C:/vcpkg or C:/Users/.../Documents) using the following:
git clone https://github.com/Microsoft/vcpkg.git
  1. Then follow the installation/setup instructions provided in the GitHub (reproduced here) to build vcpkg
./bootstrap-vcpkg.bat

The last command will output a path to the vcpkg cmake file which you will need to include in future projects to use the Titan library. Save this output, for example: '-DCMAKE_TOOLCHAIN_FILE=C:/vcpkg/scripts/buildsystems/vcpkg.cmake'

3. Download and Install Titan

To install the library using vcpkg, clone the github repository to a folder on your computer using

git clone https://github.com/jacobaustin123/Titan.git

Inside the newly downloaded Titan directory, navigate to Titan/vcpkg and copy the 'titan' directory there to the ports folder in the vcpkg installation folder from step 2. For example, if vcpkg was installed in C:/vcpkg, run:

cp -r titan C:/vcpkg/ports/titan

Then in the vcpkg installation folder, run

./vcpkg install --triplet x64-windows titan --head

This will download and install all the necessary dependencies into the vcpkg install folder. Everything is now installed, and you can use it to build a sample project. See the next page for instructions on using the library with your projects. Note that the vcpkg output you saved earlier will need to be passed to any CMake project you use the library with.

Troubleshooting

Using an IDE with the Titan library and vcpkg

Vcpkg is a convenient cross-platform dependency management system developed by Microsoft, designed to work seamlessly with CMake. In any of the installation instructions above, you saved a command like '-DCMAKE_TOOLCHAIN_FILE=/Users/username/Documents/vcpkg/scripts/buildsystems/vcpkg.cmake'. This is the command that must be passed to CMake to build a project using dependencies installed by vcpkg. This can be passed directly to vcpkg on the command-line, and for IDEs it can be included as directed below:

Using Visual Studio with Titan

To make Visual Studio compatible with Titan and CUDA, you may need to make the following changes:

  1. In Visual Studio, go to CMake/CMake Settings and generate a CMakeSettings.json file for your project. In this file, under the x64-debug and x64-release targets, you may need to add the variables section of the following example.
'name': 'x64-Release',
'variables': [
'name': 'CMAKE_TOOLCHAIN_FILE',
}
}

If this fails, change env.VCPKG_DIR to the actual path to the vcpkg directory.

  1. To work with CUDA, you may need to:

Open C:Program FilesNVIDIA GPU Computing ToolkitCUDAv9.2host_config.h Find the line if _MSC_VER < 1600 _MSC_VER > 1913 and replace it with just if _MSC_VER < 1600

  1. If you are unable to get Visual Studio 2017 to work, use Visual Studio 2015, but manually copy the Titan.dll dynamic library from the vcpkg/installed directory into your project directory. Then you should be able to include the headers found in the vcpkg/installed directory and link to the library file.

Using Microsoft Visual Studio 2015 with Titan

To install and use Microsoft Visual Studio Community 2015 with Titan, download Visual Studio Community 2015 (with Update 3) from the provided link and follow the installer instructions to install the Visual C++ compiler and v140 toolkit. You do not need to install the Visual Studio IDE or any other tools (only Visual C++ tools). You may need to subscribe for free to My Visual Studio to access older versions.

Using Microsoft Visual Studio 2017 with Titan

To install and use Microsoft Visual Studio Community 2017 with Titan, download Microsoft Visual Studio Community 2017 from Microsoft (link) and follow the installer instructions to install the Visual C++ compiler and v140 toolkit. You do not need to install the Visual Studio IDE or any other tools (only Visual C++ tools), although you may want to install the IDE if you prefer to develop in Visual Studio over CLion.

Once Visual Studio 2017 is installed, you will have to make a few changes to let it interface with CUDA. For some reason, CUDA is made incompatible with newer versions of Visual Studio, but disabling this has no adverse effects. To do this, you may need to

  • Open C:Program FilesNVIDIA GPU Computing ToolkitCUDAv9.2host_config.h
  • Find the line #if _MSC_VER < 1600 _MSC_VER > 1913 and replace it with just #if _MSC_VER < 1600

Using CLion with Titan

CLion is a cross-platform IDE developed by IntelliJ, the creators of PyCharm and IDEA. The IDE uses CMake by default, so it is ideal for including our CMake project. To build and run your project with CLion, several settings changes need to be made.

  1. First, in Settings/Build, Execution, Deployment/CMake, make sure CMake Options includes the command
-DCMAKE_TOOLCHAIN_FILE=C:/vcpkg/scripts/buildsystems/vcpkg.cmake

where the path points to the vcpkg folder. The above is an example for Windows. On Unix systems it will look like '-DCMAKE_TOOLCHAIN_FILE=/Users/username/Documents/vcpkg/scripts/buildsystems/vcpkg.cmake'. If you have vcpkg installed in a different directory, use that path instead. This is the path you saved earlier in the process.

  1. Make sure the compiler in Settings/Build, Execution, Deployment/Toolchains is set to Visual Studio 2015 or 2017 (14.0), and the architecture is set to 64-bit (amd64 on Windows). Do not use amd64_arm or any other compiler option.
  • Note that there is sometimes a bug in CLion with CUDA support that causes it to run the wrong executable - if CLion is unable to run an executable, manually run the executable found in the project directory (it will be found in the cmake-build-debug or cmake-build-release folder depending on your settings).
  • Note that CLion will sometimes find the library installed in the wrong directory, not the vcpkg version. If CLion is unable to find the library, or seems to have found the wrong version, try navigating to Program Files and Program Files (x86) and deleting any folder called 'Titan'. Then reload the CMake project (using File/Reload CMake Project). Sometimes you will also need to delete the cmake-build-debug folder, close CLion, and then reopen it and run File/Reload CMake Project.

Uninstalling

To remove the titan library on Windows, simply run

and on Unix-based systems, run

It can be reinstalled at any time using ./vcpkg install titan or ./vcpkg install titan --triplet x64-windows.

Simulation

Titan runs simulations in a Simulation object which holds references to user defined objects, graphics buffers, constraints, and other data. Data is held on the GPU, with the user interacting with CPU objects which can fetch data from the GPU, modify it, and push it back to the simulation in real time. The Simulation object controls things like the duration of the run, graphics/rendering options like the viewport, and GPU parameters.

For example:

Mass * m1 = sim.createMass(Vec(0, 0, 0));
Spring * s1 = sim.createSpring(m1, m2);
sim.setTimeStep(0.001);

Mass

The simplest discrete simulation elements. Point masses have individual physical properties (mass, position, velocity, acceleration) and can be affected by forces exerted by different sources.

Mass * m1 = sim.createMass(Vec(0, 0, 0));
sim.getMass(m1); // pull updates from simulation
cout << m1 -> vel << endl; // velocity

Spring

Springs connect pairs of masses together and apply simple Hooke's law forces. These forces are applied in parallel on the GPU, achieving a substantial performance improvement over a CPU based system.

Mass * m1 = sim.createMass(Vec(0, 0, 0));
Spring * s1 = sim.createSpring(m1, m2);
cout << s1 -> _k << endl; // spring constant
cout << m1 -> _left -> pos << endl; // position of left mass

Contaner

Masses and springs may belong to Containers which enable efficient and convenient transfers of information to and from the GPU. Once a mass or spring has been created, it can be added to a container of related objects. This container can the be pushed to or pulled from the GPU as a single unit, avoiding expensive copies and tedious boilerplate code.

Lattice * l1 = sim.createLattice(titan::Vec(0, 0, 5), titan::Vec(4, 4, 4), 20, 20, 20); // container subbclass
l1 -> translate(Vec(1, 2, 5));
l1 -> setMassValues(0.5); // set all masses in container to 0.5

Forces

Forces in Titan are defined by 3D vectors and affect masses during the simulation. Forces can be a result of several interactions:

  • Mass-Spring Hooke’s forces due to Springs connecting masses
  • Mass interactions with contact elements
  • Global accelerations (i.e. gravity) set up by the user

Contacts

Nvcc Compiler Options

Contacts are predefined simulation elements that apply forces to masses when certain positional requirements are met. Contact elements only need to be initialized to start working.

Contacts included in Titan:

  • Plane: Applies a normal force based on the masse’s displacement after breaching one face of the plane.
  • Sphere: Applies a normal force based on the masses’ displacement after breaching the sphere’s surface.
sim.createPlane(Vec(0, 0, 1), 0); // contact plane facing in the positive z direction with 0 offset
sim.createPlane(Vec(0, 0, 0), 2); // contact ball centered at origin with radius 2

Constraints

Constraints are positional limitations imposed on masses. Constraints need to be initialized and then associated to masses in order to work.

Constraints included in Titan:

  • Direction: Constraints the movement of masses to one direction only.
  • Plane: Constraints the movement of masses to a plane. The plane is defined by a normal vector and and the masse’s position at the time of its application.

Masses can also be marked as 'fixed', meaning that they cannot move, and drag can be specified on individual masses, which will be applied according to a C v^2 law.

Dynamic Simulations

The Titan simulation environment is asynchronous and dynamic. The user can make arbitrary modifications to the simulation on the run, and these will be immediately reflected in the simulation. The user can:

  • Fetch values from the GPU using sim.get(...)
  • Push values to the GPU using sim.set(...)
  • Add constraints and modify parameters of the simulation.

For example:

Lattice * l1 = sim.createLattice(titan::Vec(0, 0, 5), titan::Vec(4, 4, 4), 20, 20, 20); // container subbclass
sim.wait(0.5); // sleep at 0.5 seconds
l1 -> masses[0] -> pos = Vec(0, 0, 1); // set position of first mass.

Below are details of the Titan API, including documentation for all the major classes in Titan.

Simulation Methods

The Simulation class is the main container for running simulations in Titan. You invoke it to:

  • create Masses, Springs, and Container objects
  • delete Masses, Springs, and Container objects
  • push and pull updates from the GPU using get() and set()
  • create constraints using methods like createPlane and createBall
  • create Containers using createLattice, createCube, importFromSTL, e.g.
  • modify the simulation using commands like setAllSpringConstantValues or defaultRestLengths,
  • start the simulation using sim.start(), pause it using sim.pause(time), set breakpoints with setBreakpoint(), or wait a fixed period of time using sim.wait(time) without stopping the simulation. You can resume the simulation with sim.resume().
  • end the simulation and free memory with sim.stop()
  • control graphics, e.g. move the current viewport using setViewport or moveViewport

Cmake Cuda Compiler

Key points:

  • The simulation should be paused before calling getter or setter methods like sim.getAll().
  • All methods that modify masses or springs, like sim.setAllSpringConstantValues() must be followed by sim.setAll() if called while the simulation is running. They merely modify copies of the data on the CPU.
  • You do not need to call sim.setAll() before the simulation has started. Calling sim.start() will automatically copy all data to the GPU.

Here is a full documentation of the Simulation library:

Mass * createMass();
Spring * createSpring(Mass * m1, Mass * m2);
void deleteMass(Mass * m);
void get(Spring *s); // not really useful, no commands change springs
void set(Spring *s);
void setAll(); // set all objects
// Global constraints (can be rendered)
void createPlane(const Vec & abc, double d ); // creates half-space ax + by + cz < d
void createPlane(const Vec &abc, double d, double FRICTION_K, double FRICTION_S); // creates half-space ax + by + cz < d
void createBall(const Vec & center, double r ); // creates ball with radius r at position center
void clearConstraints(); // clears global constraints only
// Containers
void deleteContainer(Container * c);
Cube * createCube(const Vec & center, double side_length); // creates cube
Lattice * createLattice(const Vec & center, const Vec & dims, int nx = 10, int ny = 10, int nz = 10);
Beam * createBeam(const Vec & center, const Vec & dims, int nx = 10, int ny = 10, int nz = 10);
Container * importFromSTL(const std::string & path, double density = 10.0, int num_rays = 5); // density in vertices / volume
// Bulk modifications, only update CPU
void setAllMassValues(double m);
void setGlobalAcceleration(const Vec & global_acc);
void defaultRestLengths(); // makes all rest lengths equal to their current length
// Control
void stop(); // stop simulation while paused, free all memory.
void stop(double time); // stop simulation at time
void pause(double t); // pause at time t, block CPU until t.
void setBreakpoint(double time); // tell the program to stop at a fixed time (doesn't hang).
void wait(double t); // wait fixed time without stopping simulation
void waitUntil(double t); // wait until time without stopping simulation
void waitForEvent(); // wait until event (e.g. breakpoint)
double time();
Mass * getMassByIndex(int i);
std::vector<Spring *> springs;
void setViewport(const Vec & camera_position, const Vec & target_location, const Vec & up_vector);
void moveViewport(const Vec & displacement); // displace camera by vector
glm::mat4 & getProjectionMatrix(); // access glm projection matrix
}

Mass Methods

The mass object is the main particle class in Titan. A mass has a mass m, a time T, a position pos and a velocity vel. You can set any of these methods and then update the GPU with sim.set(Mass * m). You can also access the acceleration of the particle using the acceleration() method. You can add constraints or external forces as well.

double m; // mass in kg
Vec pos; // position in m
void setExternalForce(const Vec & v); // set external force applied every iteration
void addConstraint(CONSTRAINT_TYPE type, const Vec & vec, double num); // add constraint
void clearConstraints(CONSTRAINT_TYPE type); // remove constraints of a certain type
void clearConstraints(); // remove all constraints
void setDrag(double C); // set drag with coefficient C
void unfix(); // undo that
Vec color; // RGB color

Spring Methods

Springs connect masses. Each spring has a rest length, a spring constant, and can oscillate. They also support damping. You can change the rest length or modify the attached masses as desired.

enumSpringType {PASSIVE_SOFT, PASSIVE_STIFF, ACTIVE_CONTRACT_THEN_EXPAND, ACTIVE_EXPAND_THEN_CONTRACT};
class Spring {
Mass * _left; // pointer to left mass object // private
double _rest; // spring rest length (meters)
SpringType _type; // type of spring
double _damping; // damping on the spring
void setRestLength(double rest_length); // set rest length to rest_length
void defaultLength(); // sets rest length to distance between springs
void changeType(SpringType type, double omega) { _type = type; _omega = omega;}
void addDamping(double constant); // set damping coefficient
void setLeft(Mass * left); // sets left mass (attaches spring to mass 1)
void setMasses(Mass * left, Mass * right); //sets both right and left masses

Container Methods

Containers represent collections of springs or masses. Many of the built in large objects, like Lattice or Cube objects, are subclasses of the Container class. You can define new containers by subclassing the Container class.

class Container { // contains and manipulates groups of masses and springs

Cuda Project

Cuda
void translate(const Vec & displ); // translate all masses by fixed amount
// rotate all masses around a fixed axis by a specified angle with respect to the center of mass in radians
void setMassValues(double m); // set masses for all Mass objects
void setSpringConstants(double k); // set k for all Spring objects
void setRestLengths(double len); // set masses for all Mass objects
#ifdef CONSTRAINTS
void addConstraint(CONSTRAINT_TYPE type, const Vec & v, double d);
#endif
void fix(); // make all objects fixed
void add(Spring * s); // add a spring
std::vector<Spring *> springs;

Vec Methods

Vectors are the workhorse of Titan. They are 3-dimensional vectors with all the methods you'd expect.

Vec(double x, double y, double z); // initialization from x, y, and z values
Vec & operator+=(const Vec & v);
CUDA_DEVICEvoid atomicVecAdd(const Vec & v);
double & operator [] (int n);
friend Vec operator+(const Vec & v1, const Vec & v2);
friend Vec operator-(const Vec & v1, const Vec & v2);
friend Vec operator*(constdouble x, const Vec & v);
friend Vec operator*(const Vec & v, constdouble x);
friendbool operator(const Vec & v1, const Vec & v2);
friend Vec operator*(const Vec & v1, const Vec & v2);
friend Vec operator/(const Vec & v, constdouble x);
friend Vec operator/(const Vec & v1, const Vec & v2);
friend std::ostream & operator << (std::ostream & strm, const Vec & v);
void print(); // supports CUDA printing
double sum() const;
Vec normalize() const;
Vec cross(const Vec &v1, const Vec &v2); // cross product

Cuda Clion Vs

Energy conservation

Here we create a lattice bouncing on a plane under the influence of gravity. We ensure that energy is appropriately conserved over a 5 second simulation.

titan::Simulation sim;
sim.createLattice(titan::Vec(0, 0, 5), titan::Vec(4, 4, 4), 20, 20, 20);
sim.setAllSpringConstantValues(100);
sim.setGlobalAcceleration(titan::Vec(0, 0, -9.8));
sim.start();
double total_energy = titan::test::energy(sim);
double alpha = 0.7;
while (sim.time() < 5) {
avg_energy = (1 - alpha) * titan::test::energy(sim) + alpha * avg_energy;
EXPECT_NEAR(avg_energy, total_energy, total_energy * tol);
sim.resume();
}

Rotation

Here we create a lattice and repeatedly rotate it around the z axis.

titan::Simulation sim;
titan::Lattice * l2 = sim.createLattice(Vec(0, 0, 10), Vec(5, 5, 5), 10, 10, 10);
sim.setAllSpringConstantValues(1E5);
std::cout << sim.masses.size() << ' ' << sim.springs.size() << std::endl;
sim.pause(sim.time() + 1);
l2 -> rotate(Vec(0, 0, 1), 0.5);
sim.stop();
}
sim.resume();
}

This software was written by Jacob Austin and Rafael Corrales Fatou as part of a project led by Professor Hod Lipson at the Creative Machines Lab at Columbia University. This software is released under an Apache 2.0 license.

Cuda Clion Model

If using this software in published work, please cite

J. Austin, R. Corrales-Fatou, S. Wyetzner, and H. Lipson, “Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA,” ICRA 2020, May 2020.

or use the BibTex

title = {Titan: A Parallel Asynchronous Library for Multi-Agent and Soft-Body Robotics using NVIDIA CUDA},
author = {Jacob Austin, Raphael Corrales-Fatou, Soia Wyetzner, and Hod Lipson},
bookTitle = {Proc. of the {IEEE} International Conference on Robotics and Automation},

Cuda Clion Red

year = {2020},
}