Kenneth Owens

 We wish to compute the interaction between particles (bodies) given their mass and positions  Simulation is performed in time steps ◦ Forces between all bodies is computed O(n 2 ) ◦ Positions for all bodies are updated based on their current kinematics and the interaction with other bodies O(n) ◦ Time moves forward by one step

 The force between a body i and N other bodies is approximated as above by computing the interaction given their mass (m), the distance vector between them (r _ij ), and a softening factor (ε).  This is computed for all bodies with all other bodies

 Euler Method: For each particle, a discrete timestep (dt) is used to approximate the continuous kinematic equation and update the position and velocity of each particle

 Execute an n-body simulation on a distributed memory architecture with multiple GPUs on each node

 Sequential implementation of n-body simulation code ◦ Written in C ◦ Compiled using gcc-4.4 with –O3  MPI implementation ◦ Written in C ◦ Compiled using mpicc.mpich with gcc-4.4 using 0-3 ◦ Executed using mpirun.mpich on 2,5, an 10 nodes  GPU implementation ◦ Written in C with CUDA extensions ◦ Compiled using nvcc with gcc-4.4 using –O3 ◦ Executed on Nvidia 580s  MPI-GPU implementation ◦ The MPI driver above was combined with the GPU kernel implementation ◦ Compiled but not tested for correctness

 The main method of the driver calls nbody  nbody calls two externally linked function ◦ compute_forces computes the interactions ◦ update_positions updates the kinematics void nbody(vector4d_t* positions, vector4d_t* velocities, vector4d_t* current_positions, vector4d_t* current_velocities, vector3d_t* accel, size_t size, value_t dt, value_t damping, value_t softening_squared) { compute_forces(positions,accel, size, positions, size, softening_squared); update_positions(positions, velocities, current_positions, current_velocities, accel, size, dt, damping); }

 Computes the pair-wise interaction ◦ Hidden second loop in acceleration function void compute_forces(vector4d_t* positions, vector3d_t* forces, size_t positions_size, vector4d_t* sources, size_t sources_size, value_t softening_squared) { for (size_t i = 0; i < positions_size; i++) { forces[i] = acceleration(positions[i],sources,sources_size, forces[i], softening_squared); } }

 Computation for individual interaction written using c vector3d_t interaction(vector3d_t acceleration, vector4d_t body1, vector4d_t body2, value_t softening_squared) { vector3d_t force; force.x = body1.x - body2.x; force.y = body1.y - body2.y; force.z = body1.z - body2.z; float distSqr = force.x * force.x + force.y * force.y + force.z * force.z; distSqr += softening_squared; float invDist = 1.0f / sqrt(distSqr); float invDistCube = invDist * invDist * invDist; float s = body2.w * invDistCube; acceleration.x += force.x * s; acceleration.y += force.y * s; acceleration.z += force.z * s; return acceleration; }

 Updates each position based on the computed forces void update_positions(vector4d_t* positions, vector4d_t* velocities, vector4d_t* current_positions, vector4d_t* current_velocities, vector3d_t* acceleration, size_t size, value_t dt, value_t damping) { for(size_t i = 0; i < size; i++) { vector4d_t current_position = current_positions[i]; vector3d_t accel = acceleration[i]; vector4d_t current_velocity = current_velocities[i]; update_position(&positions[i], &velocities[i], current_position, current_velocity, accel, dt, damping); }

 Implements the previously shown equations void update_position(vector4d_t* position, vector4d_t* velocity, vector4d_t current_position, vector4d_t current_velocity, vector3d_t acceleration,value_t dt, value_t damping) { current_velocity.x += acceleration.x * dt; current_velocity.y += acceleration.y * dt; current_velocity.z += acceleration.z * dt; current_velocity.x *= damping; current_velocity.y *= damping; current_velocity.z *= damping; current_position.x += current_velocity.x * dt; current_position.y += current_velocity.y * dt; current_position.z += current_velocity.z * dt; *position = current_position; *velocity = current_velocity; }

 Started with the implementation from GPU Gems gpugems3_ch31.html gpugems3_ch31.html  Modified the code to work with data sizes that are larger than 256 but that are not evenly divisible by 256  Added kinematics update  Code no longer works for sizes less than 256 ◦ Needed command line specification to control grid and block size anyway

 Copies to device memory and execute the compute_force_gpu kernel ◦ Note - cudaMemAlloc truncated to fit code void compute_forces(vector4d_t* positions, vector3d_t* forces, size_t positions_size, vector4d_t* sources, size_t sources_size, value_t softening_squared) { ….. compute_forces_gpu >>(device_positions, device_forces, positions_size, device_sources, sources_size, softening_squared ); cudaThreadSynchronize(); cudaMemcpy(forces, device_forces, positions_size * sizeof(float3), cudaMemcpyDeviceToHost); cudaFree((void**)device_positions); cudaFree((void**)device_sources); cudaFree((void**)device_forces); err = cudaGetLastError(); if( cudaSuccess != err) { fprintf(stderr, "Cuda error: %s: \n", cudaGetErrorString( err) );

 Every thread computes the acceleration for its position and moves to the next block ◦ For our test sizes this only implemented cleanup for strides not divisible by 256 __global__ void compute_forces_gpu(float4* positions, float3* forces,int size, float4* sources, int sources_size, float softening_squared ) { for(int index = __mul24(blockIdx.x,blockDim.x) + threadIdx.x; index < size; index += blockDim.x * gridDim.x) { float4 pos = positions[index]; forces[index] = acceleration(pos, sources, sources_size, forces[index], softening_squared);

 Uses float3 and float4 instead of home brewed vector types  Shared memory is used 256 positions per block  Each thread strides across the grid to update a single particle __device__ float3 acceleration(float4 position, float4* positions, int size, float3 acc, float softening_squared) { extern __shared__ float4 sharedPos[]; int p = blockDim.x; int q = blockDim.y; int n = size; int numTiles = n / (p * q); for (int tile = blockIdx.y; tile < numTiles + blockIdx.y; tile++) { sharedPos[threadIdx.x+blockDim.x*threadIdx.y] = positions[WRAP(blockIdx.x + tile,gridDim.x) * p + threadIdx.x]; __syncthreads(); // This is the "tile_calculation" function from the GPUG3 article. acc = gravitation(position, acc,softening_squared); __syncthreads(); } return acc; }

 Kernel strides in the same way as the force computation  All threads update a single position simulaneously __global__ void update_positions_gpu(float4* positions, float4* velocities, float4* current_positions, float4* current_velocities, float3* forces, int size, float dt, float damping) { for(int index = __mul24(blockIdx.x,blockDim.x) + threadIdx.x; index < size; index += blockDim.x * gridDim.x) { float4 pos = current_positions[index]; float3 accel = forces[index]; float4 vel = current_velocities[index]; vel.x += accel.x * dt; vel.y += accel.y * dt; vel.z += accel.z * dt; vel.x *= damping; vel.y *= damping; vel.z *= damping; // new position = old position + velocity * deltaTime pos.x += vel.x * dt; pos.y += vel.y * dt; pos.z += vel.z * dt; // store new position and velocity positions[index] = pos; velocities[index] = vel; }

 O(n 2 )/p pipeline implementation ◦ Particles are divided among processes ◦ Particle positions are shared in a ring communication topology ◦ Force computation occurs for all particles by sending the data around the ring ◦ After all forces are computed each process updates the kinematics of its own particles

 Compiles with CPU and GPU implementations  Timings have only been collected for CPU for(size_t i = 0; i < time_steps; i++) { memcpy( sendbuf, current_positions, num_particles * sizeof(vector4d_t) ); for (pipe=0; pipe<size; pipe++) { if (pipe != size-1) { MPI_Isend( sendbuf, num_particles, mpi_vector4d_t, right, pipe, commring, &request[0] ); MPI_Irecv( recvbuf, num_particles, mpi_vector4d_t, left, pipe, commring, &request[1] ); } compute_forces(positions,accel, num_particles, positions, num_particles, softening_squared); if (pipe != size-1) MPI_Waitall( 2, request, statuses ); memcpy( sendbuf, recvbuf, num_particles * sizeof(vector4d_t) ); } update_positions(positions, velocities, current_positions, current_velocities, accel, num_particles, dt, damping); }

 Taken of float for sequential and gpu  Taken on tux for mpi  All used 10 iterations for time steps  Wallclock time was collected for comparison  Memory allocation time was omitted ◦ Except for device memory allocation and device data transfer  Timings where not collected for the code using MPI to distribute data over multiple nodes with multiple GPUs

 We achieved several orders of magnitude speed-up going to a GPU  We achieved similar results to what was obtained in GPU gems  The sequential implementation was not optimal as it did not use SSE or multiple cores – much lower than the theoretical possible FLOPs for the Xeon CPU  The MPI driver showed that task level parallelism can be exploited using distributed memory computing

 Run the MPI GPU version on Draco  FMM (Fast Multiple Method) MPI implementation  Multi-device GPU implementation