CUDA

Assignment  Subject: DES using CUDA  Deliverables: des.c, des.cu, report  Due: 12/14,

Index  What is GPU?  Programming model and Simple Example  The Environment for CUDA programming  What is DES?

What’s in a GPU?  A GPU is a heterogeneous chip multi-processor (highly tuned for graphics)

Slimming down  kjkd Idea #1: Remove components that help a single instruction stream run fast

Parallel execution Two coresFour cores Sixteen cores: 16 simultaneous instruction streams  Be able to share an instruction stream

SIMD processing Idea #2: Amortize cost/complexity of managing an instruction stream across many ALUs 16 cores = 128 ALUs

What about branches?

Throughput! Idea #3: Interleave processing of many fragments on a single core to avoid stalls caused by high latency operations

Summary: three key ideas of GPU 1. Use many “slimmed down cores” to run in parallel 2. Pack cores full of ALUs (by sharing instruction stream across groups of fragments) 3. Avoid latency stalls by interleaving execution of many groups of fragments  When one group stalls, work on another group

Programming Model  GPU is viewed as a compute device operating as a coprocessor to the main CPU (host)  Data-parallel, compute intensive functions should be off-loaded to the device  Functions that are executed many times, but independently on different data, are prime candidates  I.e. body of for-loops  A function compiled for the device is called a kernel  The kernel is executed on the device as many different threads  Both host (CPU) and device (GPU) manage their own memory, host memory and device memory

Block and Thread Allocation  Blocks assigned to SMs (Streaming Multiprocessos)  Threads assigned to PEs (Processing Elements) Each thread executes the kernel Each block has an unique block ID Each thread has an unique thread ID within the block Warp: max 32 threads GTX 280: 30SMs 1 SM: 8 SPs 1 SM: 32 warps  1024 threads Total threads: 30*1024 = 30,720

Memory model  Memory types  Registers (r/w per thread)  Local mem (r/w per thread)  Shared mem (r/w per block)  Global mem (r/w per kernel)  Constant mem (r per kernel)  Separate from CPU  CPU can access global and constant mem via PCIe bus

Simple Example (C to CUDA conversion) __global_ void ForceCalcKernel(int nbodies, struct Body *body,..) {} __global_ void Advancing Kernel(int nbodies, struct Body *body, …){} int main(…) { Body *body, *body1; … cudaMalloc((void**)&body1, sizeof(Body)*nbodies); cudaMemcpy(body1, body, sizeof(Body)*nbodies, cuda_HostToDevice); for(timestep = …) { ForceCalcKernel >(nbodies, body1, …); AdvancingKernel >(nbodies, body1, …); } cudaMemcpy(body, body1, sizeof(Body)*nbodies, cuda_DeviceToHost); cudaFree(body1); … } Indicates GPU kernel that CPU can call Separate address spaces, need two pointers Allocate memory on GPU Copy CPU data to GPU Call GPU kernel with 1block and 1thread per block Copy GPU data back to CPU

Environment  The NVCC compiler  CUDA kernels are typically stored in files ending with.cu  NVCC uses the host compiler (CL/G++) to compile CPU code  NVCC automatically handles #include’s and linking  You can download CUDA toolkit from:  uda-downloads uda-downloads

What is DES?  The archetypal block cipher  An algorithm that takes a fixed-length string of plaintext bits and transforms it through a series of complicated operations into another ciphertext bitstring of the same length  The block size is 64 bits