GPU Programming and Evolution

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Questions and Answers

What is the purpose of the function `cudaDeviceSynchronize()` in the main function?

To free allocated memory on the GPU

To ensure all device tasks are completed before continuing (correct)

To print messages from the GPU

To allocate memory on the GPU

Which line correctly initializes the CUDA kernel for vector addition?

hello();

vectorAdd(d_A, d_B, d_C, numElements); (correct)

cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);

printf( 'Hello from GPU: thread %d and block %d ', threadIdx.x, blockIdx.x );

What does the `cudaMalloc` function do in CUDA programming?

Allocates memory on the GPU (correct)

Frees previously allocated device memory

Copies data from host to device

Synchronizes CPU and GPU

In the `vectorAdd` function, what is the purpose of the statement `if (i < numElements)`?

To prevent thread overflow during calculations Signup and view all the answers

What is the default behavior of threads in a CUDA kernel if an out-of-bounds index is accessed?

The memory access fails silently Signup and view all the answers

What is `cudaMemcpyHostToDevice` used for?

To copy data from host memory to device memory Signup and view all the answers

How is the number of blocks calculated for the CUDA kernel launch?

blocksPerGrid = (numElements + threadsPerBlock) / threadsPerBlock Signup and view all the answers

What happens to the GPU memory allocated with `cudaMalloc` after its use?

It must be manually freed using <code>cudaFree</code>. Signup and view all the answers

What is the main advantage of using GPUs over CPUs for compute-intensive tasks?

GPUs are specialized for handling graphics manipulations. Signup and view all the answers

Which of the following statements best describes CUDA?

CUDA allows the GPU to function as a coprocessor to the CPU. Signup and view all the answers

What is the purpose of thread blocks in CUDA programming?

To organize threads for efficient memory access and synchronization. Signup and view all the answers

Which of the following statements about GPU architecture is true?

GPUs are more suitable for tasks that require high arithmetic/memory operation ratios. Signup and view all the answers

What is the function of Tensor Cores in a GPU?

To optimize performance for matrix operations and machine learning. Signup and view all the answers

In CUDA, what does the global keyword indicate?

The function is executed on the device and callable from the host. Signup and view all the answers

Which type of memory is most commonly used for communication between host and device in CUDA?

Global memory Signup and view all the answers

What is a key feature of GPU threads compared to CPU threads?

GPU threads are extremely lightweight and require fewer resources. Signup and view all the answers

Which CUDA feature allows multiple threads to share data efficiently?

Shared memory Signup and view all the answers

What does the term 'fine-grain SIMD parallelism' refer to in the context of GPUs?

The ability to perform the same operation on multiple data points at once. Signup and view all the answers

What is a ‘kernel’ in CUDA terminology?

A function that runs on the GPU and is called from the host. Signup and view all the answers

How does CUDA handle long-latency memory references?

By context switching between threads to hide latency. Signup and view all the answers

Which of the following is a key advantage of heterogeneous computing?

It allows the use of different types of processors for optimal performance. Signup and view all the answers

Which of the following best describes the V100 GPU’s capabilities?

It supports a wide range of machine learning and data processing tasks. Signup and view all the answers

What does 'thread batching' refer to in CUDA programming?

Launching multiple threads simultaneously in blocks. Signup and view all the answers

Study Notes

Why Choose GPUs?

GPUs have a clear advantage over CPUs in terms of speed for computation-intensive tasks like image processing and 3D rendering. This stems from the fact that GPUs have specialized processors designed for graphics manipulations which makes them faster than CPUs.

What is GPU Programming?

It involves harnessing the power of GPUs for applications beyond 3D graphics, by utilizing graphics APIs, enhancing the application's critical path.
GPU programming takes advantage of Data-Parallel algorithms to handle large amounts of data, relying on the GPU's ability to perform parallel operations (SIMD) on a massive scale and its speed in floating-point computations.

Key Milestones in GPU Evolution

GPUs have evolved from primarily being for rendering graphics to becoming programmable, eventually transitioning to becoming crucial for general-purpose computing.
The introduction of CUDA and GPGPU marked a turning point, leading to the development of GPU libraries (like cuDNN), frameworks (TensorRT), and toolkits that enhance their capabilities.
This evolution saw GPUs becoming central to AI and Deep Learning, and the advent of Heterogeneous Computing.

Heterogeneous Computing

It leverages the strengths of both CPUs and GPUs for optimal performance. Tasks are assigned to the appropriate processor based on their computational intensity – computationally intensive tasks run on GPUs while the rest of the code runs on CPUs.

Differences Between GPUs and CPUs

GPUs excel in data-intensive computation because they have more transistors devoted to computation than to caching or flow control.
The GPU's high arithmetic-to-memory operation ratio means it's well-suited for handling the demands of complex computations that require heavy mathematical operations.

GPU as a Co-processor

The GPU acts as a co-processor to the CPU (host), with its own memory (device memory), and the capability to run numerous threads in parallel.

GPU Architecture – Tesla K20

Features 15 SMX (Streaming Multiprocessor) units, each containing a multitude of cores, contributing to its processing capabilities along with its 7.1 billion transistors.

Streaming Multiprocessor K20 (FERMI)

It is the central processing unit within the GPU, containing multiple cores, a shared memory space, and a control unit. It handles the execution of threads, managing their scheduling and data access.

Understanding GPU Architecture

It encompasses a hierarchy of processing units. The core units (CUDA cores) are responsible for execution, organized into streaming multiprocessors (SMs) which are grouped into Graphics Processing Clusters (GPCs). Each SM has shared and local memory and its own controller.

V100 GPU

A powerful GPU utilizing the Volta architecture, it has 6 GPCs, 7 TPCs (Texture Processing Clusters), and memory controllers.
Contains a total of 5120 cores per GPU, each SM consisting of 64 FP32, 64 INT32, 32 FP64 cores, and 8 Tensor cores.
Offers incredible performance with teraflops of processing capabilities for FP32 , FP64, and dedicated tensor operations.

Tensor Cores

Specialized units within GPUs, accelerate matrix multiplications (commonly used in AI and Deep Learning) by handling computations for specific data types (like FP16 or INT8) much faster than traditional cores.
They enhance the performance of applications heavily revolving around matrix operations.

Tensor Cores with Turing

Represent a further development of the Tensor core technology, integrating them into the SMs (Streaming Multiprocessor) of the Turing architecture.
There are 4 Tensor cores per SM.

Execution Model

A multi-threaded approach where a single GPU kernel (function) gets executed by a multitude of threads, distributed across multiple thread blocks (groups of threads). Each thread operates on its own data, allowing for parallel processing.

Data Model

Data is organized within the GPU's memory space, with each thread having access to registers, local memory, and shared memory.
Global memory serves as the main communication avenue between the host CPU and the GPU device.

Programming Models for GPGPU

Different models exist for programming GPUs to take advantage of their parallel computing power. Some popular models include CUDA, OpenCL, and DirectCompute, each with its own approach and strengths.

CUDA

NVIDIA's software platform for GPU programming. It enables programmers to write code that executes on NVIDIA GPUs, leveraging their parallel processing power.
It views the GPU as a co-processor, with its own memory and many threads running in parallel, effectively hiding memory access latencies.
CUDA uses kernels to execute the data-parallel parts of an application.

Applications of CUDA

CUDA is widely used in diverse fields like:
- High-Performance Computing (HPC)
- Scientific Simulation
- Machine Learning (particularly Deep Learning)
- Image Processing
- 3D Graphics
- Cryptography

Thread Batching: Grids and Blocks

CUDA uses the concept of grids and blocks to organize threads. A grid is a collection of thread blocks, and each block is a group of threads that can cooperate with each other.
Threads in the same block can share data and synchronize their execution through the efficient use of shared memory, a low latency memory space.

CUDA Function Declaration

CUDA uses function qualifiers (__global__, __device__, __host__) to specify where a function should be executed (device or host).
__global__ defines a kernel function, which can only be called from the host.
__device__ functions can be called from both the host and the device.
__host__ functions can only be called from the host.

CUDA Device Memory Space Overview

There are several memory types in the device space:
- Registers: Very fast memory that is used for storing temporary data.
- Local Memory: Slower than registers but faster than global and shared memory. Each thread has its own local memory space and can access it.
- Shared Memory: A fast memory space accessible by all threads within a block. Shared memory is crucial for thread synchronization and efficient data sharing between threads performing tasks.
- Global Memory: The main memory accessible by all threads on the GPU, but it is the slowest memory type.
- Constant Memory: Read-only memory that can be used to store data that doesn't change.
- Texture Memory: Read-only memory that is optimized for texture filtering and addressing.

Global, Constant, and Texture Memories (Long Latency Accesses)

Access to global, constant, and texture memory can be slow compared to local, registers, and shared memory. This is because these memory types are not directly tied to the execution units, and accessing them introduces additional latency.

Calling Kernel Function - Thread Creation

To execute a kernel function, it's essential to set an execution configuration comprising:
- Number of thread blocks in the grid
- Number of threads per block
- Size of shared memory used by the blocks
- Dim3 is a structure representing a 3D dimension.

Automatic Scalability

CUDA inherently offers automatic scalability. By adjusting the grid and block configurations, the kernel execution can effectively adapt to the available GPU resources, maximizing parallelism by allocating the appropriate number of blocks and threads to the GPU. This ensures optimal utilization of the GPU's capacity for different tasks.

Provides instructions on how to login into the V100 GPU system using an internal IP address and credentials.

CUDA: Hello World! example

Demonstrates basic CUDA programming, starting with a simple example of a kernel function that prints "Hello from GPU," showing the basic steps of launching a kernel, executing it on the GPU, and retrieving the results.

CUDA: Vector Addition

Demonstrates performing a vector addition operation using CUDA. It involves:
- Allocating memory on the GPU
- Copying data from the host to the device
- Launching a vector addition kernel
- Copying data back from the device to the host
- Freeing memory
It shows the fundamental concepts of CUDA memory management, kernel execution, and data transfer.

Single-Precision A·X Plus Y

Illustrates the implementation of the SAXPY operation (Single-Precision A·X Plus Y), a common computation in scientific computing and machine learning. It's a simple loop based approach.

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Description

This quiz explores the advantages of GPUs over CPUs for computation-intensive tasks and delves into the evolution of GPU programming. Learn about the milestones in GPU development, including the shift from graphics rendering to general-purpose computing. Test your knowledge on GPU architectures, programming languages, and their applications in various fields.