CS179: GPU Programming Lecture 8: More CUDA Runtime

Today  CUDA arrays for textures  CUDA runtime  Helpful CUDA functions

CUDA Arrays  Recall texture memory  Used to store large data  Stored on GPU  Accessible to all blocks, threads

CUDA Arrays  Used Texture memory for buffers (lab 3)  Allows vertex data to remain on GPU  How else can we access texture memory?  CUDA arrays

CUDA Arrays  Why CUDA arrays over normal arrays?  Better caching, 2D caching  Spatial locality  Supports wrapping/clamping  Supports filtering

CUDA Linear Textures  “Textures” but in global memory  Usage:  Step 1: Create texture reference  texture tex  TYPE = float, float3, int, etc.  Step 2: Bind memory to texture reference  cudaBindTexture(offset, tex, devPtr, size);  Step 3: Get data on device via tex1Dfetch  tex1DFetch(tex, x);  x is the byte where we want to read!  Step 4: Clean up after finished  cudaUnbindTexture(&tex)

CUDA Linear Textures  Texture reference properties:  texRef  type = float, int, float3, etc.  dim = # of dimensions (1, 2, or 3)  mode =  cudaReadModeElementType: standard read  cudaReadModeNormalizedFloat: maps 0->0.0, 255->1.0 for ints->floats

CUDA Linear Textures  Important warning:  Textures are in a global space of memory  Threads can read and write to texture at same time  This can cause synchronization problems!  Do not rely on thread running order, ever

CUDA Linear Textures  Other limitations:  Only 1D, can make indexing and caching a bit less convenient  Pitch may be not ideal for 2D array  Not read-write  Solution: CUDA arrays

CUDA Arrays  Live in texture memory space  Access via texture fetches

CUDA Arrays  Step 1: Create channel description  Tells us texture attributes  cudaCreateChannelDesc(int x, int y, int z, int w, enum mode)  x, y, z, w are number of bytes per component  mode is cudaChannelFormatKindFloat, etc.

CUDA Arrays  Step 2: Allocate memory  Must be done dynamically  Use cudaMallocArray(cudaArray **array, struct desc, int size)  Most global memory functions work with CUDA arrays too  cudaMemcpyToArray, etc.

CUDA Arrays  Step 3: Create texture reference  texture texRef -- just as before  Parameters must match channel description where applicable  Step 4: Edit texture settings  Settings are encoded as texRef struct members

CUDA Arrays  Step 5: Bind the texture reference to array  cudaBindTextureToArray(texRef, array)  Step 6: Access texture  Similar to before, now we have more options:  tex1DFetch(texRef, x)  tex2DFetch(texRef, x, y)

CUDA Arrays  Final Notes:  Coordinates can be normalized to [0, 1] if in float mode  Filter modes: nearest point or linear  Tells CUDA how to blend texture  Wrap vs. clamp:  Wrap: out of bounds accesses wrap around to other side  Ex.: (1.5, 0.5) -> (0.5, 0.5)  Clamp: out of bounds accesses set to border value  Ex.: (1.5, 0.5) -> (1.0, 0.5)

CUDA Arrays point sampling linear sampling

CUDA Arrays wrap clamp

CUDA Runtime  Nothing new, every function cuda____ is part of the runtime  Lots of other helpful functions  Many runtime functions based on making your program robust  Check properties of card, set up multiple GPUs, etc.  Necessary for multi-platform development!

CUDA Runtime  Starting the runtime:  Simply call a cuda_____ function!  CUDA can waste a lot of resources  Stop CUDA with cudaThreadExit()  Called automatically on CPU exit, but you may want to call earlier

CUDA Runtime  Getting devices and properties:  cudaGetDeviceCount(int * n);  Returns # of CUDA-capable devices  Can use to check if machine is CUDA-capable!  cudaSetDevice(int n)  Sets device n to the currently used device  cudaGetDeviceProperties(struct *devProp prop, int n);  Loads data from device n into prop

Device Properties  char name[256]: ASCII identifier of GPU  size_t totalGlobalMem: Total global memory available  size_t sharedMemPerBlock: Shared memory available per multiprocessor  int regsPerBlock: How many registers we have per block  int warpSize: size of our warps  size_t memPitch: maximum pitch allowed for array allocation  int maxThreadsPerBlock: maximum number of threads/block  int maxThreadsDim[3]: maximum sizes of a block

Device Properties  int maxGridSize[3]: maximum grid sizes  size_t totalConstantMemory: maximum available constant memory  int major, int minor: major and minor versions of CUDA support  int clockRate: clock rate of device in kHz  size_t textureAlignment: memory alignment required for textures  int deviceOverlap: Does this device allow for memory copying while kernel is running? (0 = no, 1 = yes)  int multiprocessorCount: # of multiprocessors on device

Device Properties  Uses?  Actually get values for memory, instead of guessing  Program to be accessible for multiple systems  Can get the best device

Device Properties  Getting the best device:  Pick a metric (Ex.: most multiprocessors could be good) int num_devices, device; cudaGetDeviceCount(&num_devices); if (num_devices > 1) { int max_mp = 0, best_device = 0; for (device = 0; device < num_devices; device++) { cudaDeviceProp prop; cudaGetDeviceProperties(&prop, device); int mp_count = prop.multiProcessorCount; if (mp_count > max_mp) { max_mp = mp_count; best_device = device; } cudaSetDevice(best_device); }

Device Properties  We can also use this to launch multiple GPUs  Each GPU must have its own host thread  Multithread on CPU, each thread calls different device  Set device on thread using cudaSetDevice(n);

CUDA Runtime  Synchronization Note:  Most calls to GPU/CUDA are asynchronous  Some are synchonous (usually things dealing with memory)  Can force synchronization:  cudaThreadSynchronize()  Blocks until all devices are done  Good for error checking, timing, etc.

CUDA Events  Great for timing!  Can place event markers in CUDA to measure time  Example code: cudaEvent_t start, stop; cudaCreateEvent(&start); cudaCreateEvent(&stop); cudaEventRecord(start, 0); // DO SOME GPU CODE HERE cudaEventRecord(stop, 0); cudaEventSynchronize(stop); float elapsed_time; cudaEventElapsedTime(&elapsed_time, start, stop);

CUDA Streams  Streams manage concurrency and ordering  Ex.: call malloc, then kernel 1, then kernel 2, etc.  Calls in different streams are asynchronous!  Don’t know when each stream is where in code

Using Streams  Create stream  cudaStreamCreate(cudaStream_t *stream)  Copy memory using async calls:  cudaMemcpyAsync(…, cudaStream_t stream)  Call in kernel as another parameter:  kernel >>  Query if stream is done:  cudaStreamQuery(cudaStream_t stream)  returns cudaSuccess if stream is done, cudaErrorNotReady otherwise  Block process until a stream is done:  cudaStreamSynchronize(cudaStream_t stream)  Destroy stream & cleanup:  cudaStreamDestroy(cudaStream_t stream)

Using Streams  Example: cudaStream_t stream[2]; for (int i = 0; i < 2; ++i) cudaStreamCreate(&stream[i]); for (int i = 0; i < 2; ++i) cudaMemcpyAsync(inputDevPtr + i * size, hostPtr + i * size, size, cudaMemcpyHostToDevice, stream[i]); for (int i = 0; i < 2; ++i) myKernel >>(outputDevPtr + i * size, inputDevPtr + i * size, size); for (int i = 0; i < 2; ++i) cudaMemcpyAsync(hostPtr + i * size, outputDevPtr + i * size, size, cudaMemcpyDeviceToHost, stream[i]); cudaThreadSynchronize();

Next Time  Lab 4 Recitation:  3D Textures  Pixel Buffer Objects (PBOs)  Fractals!