![]() ![]() Delirious with fever, the 22-year-old aspiring architect hallucinated prisons. Malaria, a seasonal epidemic that killed thousands of Italians every year until the middle of the 20th century, afflicts sufferers with high fever, chills, and pounding headaches, among other nasty symptoms. In 1742, the Italian engraver Giovanni Battista Piranesi fell ill. ![]() Int idx = blockIdx.x * blockDim.x + threadIdx.Sign up for our newsletter to get submission announcements and stay on top of our best work. _global_ void VectorAdd(float *da, float *db, float *dc, int N) Copy data from device-memory to host-memory.Execute _global_ declared funtions on GPU.Copy data from host-memory to device-memory.Generally speaking, there are usually 3 steps to write a CUDA program. If we want to let host wait for device to finish the kernel functions, cudaDeviceSynchronize should be called in host code. These two types of threads are parallelized, the host will NOT wait for device to finish its job. "Device" denote those threads running on GPU cores."Host" denote the thread running in CPU environment.There are two roles in CUDA program, host and device. On current GPUs, a thread block may contain up to 1024 threads. There is a limit to the number of threads per block, since all threads of a block are expected to reside on the same processor core and must share the limited memory resources of that core. Kernel invocation with one block of N * N * 1 threads For a three-dimensional block of size (Dx, Dy, Dz), the thread ID of a thread of index (x, y, z) is (x + y * Dx + z * Dx * Dy).Īs an example, the following code adds two matrices A and B of size N x N and stores the result into matrix C: // Kernel definition.For a two-dimensional block of size (Dx, Dy),the thread ID of a thread of index (x, y) is (x + y * Dx) it's similar to a two-dimension-array.For a one-dimensional block, they are the same.The index of a thread and its thread ID relate to each other in a straightforward way: We will introduce this in details by some examples in the latter blogs. All threads have access to the same global memory.Each thread block has shared memory visible to all threads of the block and with the same lifetime as the block.barrier synchronization - that are simply exposed to the programmer as a minimal set of language extensions.We can have the conclusion that "gird > block > warp > thread". One block can be executed on different SMs at the same time, since it may contains multiple warps.Most GPUs (excepting Turing) allow a hardware limit of 64 warps per SM, as well as 2048 threads per SM (these are consistent). ![]() Suppose I have 1024 threads per block (i.e.For each scheduling, it will put some warps into SMs. Warp - A group of 32 threads in thread block is called a warp.define gird = (2x3) and block = (4x5), grid denotes one block, block denotes one thread. From the perspective of programmer, we can group the threads into 2D shape (3D shape is okay if you want)., that represents to the physical GPU.įrom the perspective of software, there are 4 key concepts: ls /dev/nvidia*, you will see /dev/nvidia0, /dev/nvidia1.Device - "Device" usually refers to a physical GPU on the machine.One physical contains multiple SMs, and one SM can execute multiple GPU-threads.One CPU core can execute multiple threads. As we know, one physical CPU contains multiple core. SM (Streaming Multiprocessor) - A SM contains one fetch-decode-unit, multiple SPs (execution units), multiple groups of registers, and cache.SP (Streaming Processor/Streaming Core) - It's similar to a scalar core in CPU.GPU can hide memory access latencies with computation, instead of relying on large data caches and complex flow control to avoid long memory access latencies, both of which are expensive in terms of transistors.įrom the perspective of hardware, there are some key words we need to know.GPU devotes more transistors to data processing, e.g., floating-point computations, is beneficial for highly parallel computations.HardwareĬompared with CPU, GPU is specialized for highly parallel computations and therefore designed such that more transistors are devoted to data processing rather than data caching and flow control. In this section, the architecture of GPU will be introduced from two perspective, hardware and software. From more details, you should read CUDA Guide - Nvidia. In this blog, we will introduce the architecture of GPU from the programmers' perspective, and give some examples of CUDA programming. ![]()
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