Memory Paged locked memory (Pinned memory) – Useful in concurrent kernel execution – Use cudaHostAlloc() and cudaFreeHost() allocate and free page-locked host memory Mapped memory – A block of page-locked host memory can also be mapped into the address space of the device by passing flag cudaHostAllocMapped to cudaHostAlloc()
Zero-Copy Zero-Copy enables GPU threads to directly access host memory. Requires mapped pinned (non-pageable) memory. Zero copy can be used in place of streams because kernel-originated data transfers automatically overlap kernel execution without the overhead of setting up and determining the optimal number of streams Use cudaSetDeviceFlags() with cudaDeviceMapHost()
Introduction Stream programming (pipeline) is a useful parallel pattern. Data transfer from host to device is a major performance bottleneck in GPU programming CUDA provides support for asynchronous data transfer and kernel executions. A stream is simply a sequence of operations that are performed in order on the device. Allow concurrent execution of kernels. Maximum number of concurrent kernel calls to be launched is 16.
Event processing Events are used for – Monitor device behavior – Accurate rate timing cudaEvent_t e cudaEventCreate(&e); cudaEventDestroy(e);
Event processing cudaEventRecord() records and event associated with a stream. cudaEventElapsedTime() finds the time between two input events. cudaEventSynchronize() blocks until the event has actually been recorded cudaEventQuery() Check status of an event. cudaStreamWaitEvent() makes all future work submitted to stream wait until event reports completion before beginning execution. cudaEventCreateWithFlags() create events with flags e.g:- cudaEventDefault, cudaEventBlockingSync
Stream Synchronization cudaDeviceSynchronize() waits until all preceding commands in all streams of all host threads have completed. cudaStreamSynchronize() takes a stream as a parameter and waits until all preceding commands in the given stream have completed cudaStreamWaitEvent() takes a stream and an event as parameters and makes all the commands added to the given stream after the call to cudaStreamWaitEvent() delay their execution until the given event has completed. cudaStreamQuery() provides applications with a way to know if all preceding commands in a stream have completed.
Multiple device access cudaSetDevice(devID) – Devise selection within the code by specifying the identifier and making CUDA kernels run on the selected GPU.
Peer to peer memory Access Peer-to-Peer Memory Access – Only on Tesla or above – cudaDeviceEnablePeerAccess() to check peer access
Peer to peer memory Copy Using cudaMemcpyPeer() – works for Geforce 480 and other GPUs.
Programming multiple GPUs The most efficient way to use multiple GPUs is to use host threads for multiple GPUs and divide the work among them. – E.g- pthreads Need to combine the parallelism of multi-core processor to in conjunction with multiple GPU's. In each thread use cudaSetDevice() to specify the device to run.
Multiple GPU For each computation on GPU create a separate thread and specify the device a CUDA kernel should run. Synchronize both CPU threads and GPU.