我的站点

一个系统软件工程师的随手涂鸦

Tag: GPU (Page 2 of 2)

CUDA编程笔记(7)——thread hierarchy

这篇笔记摘自Professional CUDA C Programming

When a kernel function is launched from the host side, execution is moved to a device where a large number of threads are generated and each thread executes the statements specified by the kernel function. CUDA exposes a thread hierarchy abstraction to enable you to organize your threads. This is a two-level thread hierarchy decomposed into blocks of threads and grids of blocks:

capture

All threads spawned by a single kernel launch are collectively called a grid. All threads in a grid share the same global memory space. A grid is made up of many thread blocks. A thread block is a group of threads that can cooperate with each other using:
➤ Block-local synchronization
➤ Block-local shared memory
Threads from different blocks cannot cooperate.

Threads rely on the following two unique coordinates to distinguish themselves from each other:
➤ blockIdx (block index within a grid)
➤ threadIdx (thread index within a block)
These variables appear as built-in, pre-initialized variables that can be accessed within kernel functions. When a kernel function is executed, the coordinate variables blockIdx and threadIdx are assigned to each thread by the CUDA runtime. Based on the coordinates, you can assign portions of data to different threads. The coordinate variable is of type uint3, a CUDA built-in vector type, derived from the basic integer type.

CUDA organizes grids and blocks in three dimensions. The dimensions of a grid and a block are specifed by the following two built-in variables:
➤ blockDim (block dimension, measured in threads)
➤ gridDim (grid dimension, measured in blocks)
These variables are of type dim3, an integer vector type based on uint3 that is used to specify dimensions. When defining a variable of type dim3, any component left unspecified is initialized to 1.

blockIdx&threadIdxuint3类型,含义是坐标,所以下标从0开始;blockDim&gridDimdim3类型,含义是维度,即用来计算block中有多少个thread,当前grid中包含多少个block,因此默认值是1

There are two distinct sets of grid and block variables in a CUDA program: manually-defined dim3 data type and pre-defined uint3 data type. On the host side, you define the dimensions of a grid and block using a dim3 data type as part of a kernel invocation. When the kernel is executing, the CUDA runtime generates the corresponding built-in, pre-initialized grid, block, and thread variables, which are accessible within the kernel function and have type uint3. The manually-defined grid and block variables for the dim3 data type are only visible on the host side, and the built-in, pre-initialized grid and block variables of the uint3 data type are only visible on the device side.

It is important to distinguish between the host and device access of grid and block variables. For example, using a variable declared as block from the host, you define the coordinates and access them as follows:
block.x, block.y, and block.z
On the device side, you have pre-initialized, built-in block size variable available as:
blockDim.x, blockDim.y, and blockDim.z
In summary, you define variables for grid and block on the host before launching a kernel, and access them there with the x, y and z fields of the vector structure from the host side. When the kernel is launched, you can use the pre-initialized, built-in variables within the kernel.

下面这3页摘自Learn CUDA In An Afternoon

1

2

3

CUDA编程笔记(6)——RUNTIME API VS DRIVER API

这篇笔记摘自Professional CUDA C Programming

CUDA provides two API levels for managing the GPU device and organizing threads:
➤ CUDA Driver API
➤ CUDA Runtime API
The driver API is a low-level API and is relatively hard to program, but it provides more control over how the GPU device is used. The runtime API is a higher-level API implemented on top of the driver API. Each function of the runtime API is broken down into more basic operations issued to the driver API.

There is no noticeable performance difference between the runtime and driver APIs. How your kernels use memory and how you organize your threads on the device have a much more pronounced effect.

These two APIs are mutually exclusive. You must use one or the other, but it is not possible to mix function calls from both.

capture

CUDA编程笔记(5)——CUDA程序结构

这篇笔记摘自Professional CUDA C Programming

A typical CUDA program structure consists of five main steps:
1. Allocate GPU memories.
2. Copy data from CPU memory to GPU memory.
3. Invoke the CUDA kernel to perform program-specific computation.
4. Copy data back from GPU memory to CPU memory.
5. Destroy GPU memories.

CUDA exposes you to the concepts of both memory hierarchy and thread hierarchy, extending your ability to control thread execution and scheduling to a greater degree, using:
➤ Memory hierarchy structure
➤ Thread hierarchy structure
For example, a special memory, called shared memory, is exposed by the CUDA programming model. Shared memory can be thought of as a software-managed cache, which provides great speedup by conserving bandwidth to main memory. With shared memory, you can control the locality of your code directly.

When writing a parallel program in ANSI C, you need to explicitly organize your threads with either pthreads or OpenMP, two well-known techniques to support parallel programming on most processor architectures and operating systems. When writing a program in CUDA C, you actually just write a piece of serial code to be called by only one thread. The GPU takes this kernel and makes it parallel by launching thousands of threads, all performing that same computation. The CUDA programming model provides you with a way to organize your threads hierarchically. Manipulating this organization directly affects the order in which threads are executed on the GPU. Because CUDA C is an extension of C, it is often straightforward to port C programs to CUDA C. Conceptually, peeling off the loops of your code yields the kernel code for a CUDA C implementation.

CUDA abstracts away the hardware details and does not require applications to be mapped to traditional graphics APIs. At its core are three key abstractions: a hierarchy of thread groups, a hierarchy of memory groups, and barrier synchronization, which are exposed to you as a minimal set of language extensions.

 

CUDA编程笔记(4)——CPU THREAD VS GPU THREAD

这篇笔记摘自Professional CUDA C Programming

Threads on a CPU are generally heavyweight entities. The operating system must swap threads on and off CPU execution channels to provide multithreading capability. Context switches are slow and expensive.
Threads on GPUs are extremely lightweight. In a typical system, thousands of threads are queued up for work. If the GPU must wait on one group of threads, it simply begins executing work on another.
CPU cores are designed to minimize latency for one or two threads at a time, whereas GPU cores are designed to handle a large number of concurrent, lightweight threads in order to maximize throughput.
Today, a CPU with four quad core processors can run only 16 threads concurrently, or 32 if the CPUs support hyper-threading.
Modern NVIDIA GPUs can support up to 1,536 active threads concurrently per multiprocessor. On GPUs with 16 multiprocessors, this leads to more than 24,000 concurrently active threads.

CUDA编程笔记(3)——Heterogeneous architecture

这篇笔记摘自Professional CUDA C Programming

A typical heterogeneous compute node nowadays consists of two multicore CPU sockets and two or more many-core GPUs. A GPU is currently not a standalone platform but a co-processor to a CPU. Therefore, GPUs must operate in conjunction with a CPU-based host through a PCI-Express bus. That is why, in GPU computing terms, the CPU is called the host and the GPU is called the device.

capture

A heterogeneous application consists of two parts:
➤ Host code
➤ Device code
Host code runs on CPUs and device code runs on GPUs. An application executing on a heterogeneous platform is typically initialized by the CPU. The CPU code is responsible for managing the environment, code, and data for the device before loading compute-intensive tasks on the device.

There are two important features that describe GPU capability:
➤ Number of CUDA cores
➤ Memory size
Accordingly, there are two different metrics for describing GPU performance:
➤ Peak computational performance
➤ Memory bandwidth
Peak computational performance is a measure of computational capability, usually defined as how many single-precision or double-precision floating point calculations can be processed per second. Peak performance is usually expressed in gflops (billion floating-point operations per second) or tflops (trillion floating-point calculations per second). Memory bandwidth is a measure of the ratio at which data can be read from or stored to memory. Memory bandwidth is usually expressed in gigabytes per second, GB/s.

CUDA编程笔记(2)——GPU core VS CPU core

这篇笔记摘自Professional CUDA C Programming

Even though many-core and multicore are used to label GPU and CPU architectures, a GPU core is quite different than a CPU core.
A CPU core, relatively heavy-weight, is designed for very complex control logic, seeking to optimize the execution of sequential programs.
A GPU core, relatively light-weight, is optimized for data-parallel tasks with simpler control logic, focusing on the throughput of parallel programs.

 

什么是CUDA?

从这篇文章介绍了什么是CUDA

CUDA® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU).

CUDANVIDIA提供的一个并行计算平台和模型,可以让程序更好地利用GPU。下面这段话则很好地解释了什么是“GPU computing”:

Using high-level languages, GPU-accelerated applications run the sequential part of their workload on the CPU – which is optimized for single-threaded performance – while accelerating parallel processing on the GPU. This is called “GPU computing.”

CUDA Zone展示了CUDA提供的产品,NVML也包含在其中。

CPU,GPU和GPGPU的区别

下面摘自Why are we still using CPUs instead of GPUs?

GPUs have far more processor cores than CPUs, but because each GPU core runs significantly slower than a CPU core and do not have the features needed for modern operating systems, they are not appropriate for performing most of the processing in everyday computing. They are most suited to compute-intensive operations such as video processing and physics simulations.

GPU(Graphics Processing Unit)core数量比CPU的多,它是显卡(video card)的CPU。由于它的指令集不如CPU强大,但是core数量多,所以适合做一些相对简单的,计算密集性的运算:比如图像处理等等。GPGPU(General Purpose Graphics Processing Unit)则不仅仅只做图像处理的相关运算,也会做一些一般性的运算。

更新:计算机屏幕上的图像是如何显示出来的?这个帖子给了很好的解释:

The GPU has a series of registers that the BIOS maps. These permit the CPU to access the GPU’s memory and instruct the GPU to perform operations. The CPU plugs values into those registers to map some of the GPU’s memory so that the CPU can access it. Then it loads instructions into that memory. It then writes a value to a register that tells the GPU to execute the instructions the CPU loaded into its memory.

The information consists of the software that the GPU needs to run. This software is bundled with the driver and then the driver handles the responsibility split between the CPU and GPU (by running portions of its code on both devices).

The driver then manages a series of “windows” into GPU memory that the CPU can read from and write to. Generally, the access pattern involves the CPU writing instructions or information into mapped GPU memory and then instructing the GPU, through a register, to execute those instruction or process that information. The information includes shader logic, textures, and so on.

简单地讲,CPU会把要显示的图像和指令存到显卡(video card)的register中,然后通知GPU(显卡上的CPU)去执行画图命令。 此外,wiki百科上的这张图形象地描述了整个过程:

CUDA_processing_flow_(En)

参考资料:

 

Page 2 of 2

Powered by WordPress & Theme by Anders Norén