First things first, what dynamic parallelism is and why it’s so ***** cool for simulating particle collisions in our favorite field of study: physics! Dynamic parallelism allows us to launch child kernels from within a parent kernel on the same GPU device. This means that we can break down complex simulations into smaller, more manageable tasks that run simultaneously and independently.
Now, let’s get technical for just a moment. In order to use dynamic parallelism in CUDA, you need to have at least one NVIDIA Kepler or later GPU device with compute capability 3.5 or higher. This is because the feature was introduced in version 6 of the CUDA toolkit and requires support from the hardware itself.
So how do we use dynamic parallelism for high energy physics simulations? Well, let’s say you have a simulation that involves calculating the trajectory of multiple particles colliding with each other. Instead of launching one massive kernel to handle all of these calculations at once, you can break it down into smaller kernels that run in parallel on different parts of the GPU device.
Here’s an example code snippet for a simple particle collision simulation using dynamic parallelism:
“`c++
// This is a c++ script for a simple particle collision simulation using dynamic parallelism.
// This function is the parent kernel that will be launched on the GPU device.
// It takes in the number of particles as a parameter.
__global__ void parentKernel(int numParticles) {
// Get the index of this thread within its block.
// blockIdx.x represents the index of the current block, blockDim.x represents the number of threads in each block, and threadIdx.x represents the index of the current thread within its block.
int idx = blockIdx.x * blockDim.x + threadIdx.x;
// Check if we’re working on a valid particle.
if (idx < numParticles) {
// Declare variables for position and velocity of first particle.
float x1, y1, z1, vx1, vy1, vz1, m1;
// Declare variables for position and velocity of second particle.
float x2, y2, z2, vx2, vy2, vz2, m2;
// Load the positions and velocities of both particles from global memory into shared memory.
// Shared memory is a faster memory space that is shared between threads within a block.
// In this case, we are loading 8 floats (32 bytes) into shared memory.
__shared__ float s_pos[8], s_vel[8];
// Calculate the offset for the current particle in global memory.
int offset = idx * 16;
// Load position of first particle from global memory into shared memory.
// __ldg() is a CUDA function that loads data from global memory into shared memory.
s_pos[0] = x1 = __ldg(&globalPositions[offset + 0]);
s_pos[1] = y1 = __ldg(&globalPositions[offset + 4]);
s_pos[2] = z1 = __ldg(&globalPositions[offset + 8]);
// Load velocity of first particle from global memory into shared memory.
s_vel[0] = vx1 = __ldg(&globalVelocities[offset + 12]);
s_vel[1] = vy1 = __ldg(&globalVelocities[offset + 16]);
s_vel[2] = vz1 = __ldg(&globalVelocities[offset + 20]);
// Calculate the offset for the second particle in global memory.
// In this case, we are using integer division to get the index of the second particle in the same block as the first particle.
int offset2 = idx / numParticles * 16;
// Load the positions and velocities of second particle from global memory into shared memory.
s_pos[3] = x2 = __ldg(&globalPositions[offset2 + 0]);
s_pos[4] = y2 = __ldg(&globalPositions[offset2 + 4]);
s_pos[5] = z2 = __ldg(&globalPositions[offset2 + 8]);
s_vel[3] = vx2 = __ldg(&globalVelocities[offset2 + 12]);
s_vel[4] = vy2 = __ldg(&globalVelocities[offset2 + 16]);
s_vel[5] = vz2 = __ldg(&globalVelocities[offset2 + 20]);
// Calculate the distance between the two particles and check if they're colliding.
// We are using the shared memory values to calculate the distance.
float dx = s_pos[0] - s_pos[3];
float dy = s_pos[1] - s_pos[4];
float dz = s_pos[2] - s_pos[5];
// Calculate the squared distance between the two particles.
float distSqr = dx * dx + dy * dy + dz * dz;
// Check if they're colliding within a certain threshold.
if (distSqr < 100) {
// Get the index of this particle in its parent block.
// In this case, we are using integer division to get the index of the current particle in its parent block.
int childIdx = idx / numParticles;
// Launch a child kernel to handle the collision calculation for this pair of particles.
// Dynamic parallelism allows us to launch a kernel from within another kernel.
// In this case, we are launching a child kernel to handle the collision calculation for this pair of particles.
// We are using a grid of 1 block and a block of 64 threads.
// The child kernel will handle collisions between two particles using a warp of 32 threads.
dim3 childGrid(1, 1);
dim3 childBlock(64, 1, 1);
// Launch the child kernel from within the parent kernel using dynamic parallelism.
// The parent kernel is passed as a parameter to the child kernel.
// We are also dividing the number of particles by 8 to handle 8 particles at a time in the child kernel.
parentKernel
}
}
}
“`
In this example code snippet, we’re launching a child kernel for each pair of colliding particles. The child kernel is launched from within the parent kernel using dynamic parallelism, which allows us to break down our simulation into smaller tasks that run simultaneously and independently on different parts of the GPU device.
Dynamic parallelism in CUDA for high energy physics simulations. It’s like a supercharged particle accelerator for your code.