Understanding GPU Usage — Enhancing High-Performance Computing | by Brandon Kang | Jun, 2024

The development of contemporary expertise has elevated the necessity for information processing and high-performance computing. This demand has highlighted the significance of GPUs (Graphics Processing Models), which have been initially developed for graphics rendering however at the moment are important for large-scale information processing and high-performance computing throughout numerous fields.

A GPU is a graphics processing unit designed primarily for quick rendering of photographs and movies. It consists of quite a few cores, enabling environment friendly parallel processing. Not like a CPU, which is designed with just a few high-performance cores optimized for sequential duties, a GPU is constructed with hundreds of smaller cores geared toward dealing with parallel processing duties.

In trendy information processing and high-performance computing, parallel processing is essential. GPUs excel on this space, making them indispensable in fields resembling synthetic intelligence and machine studying, the place large datasets and complicated fashions require fast coaching. GPUs are additionally invaluable in scientific computation, video encoding, and different high-performance computing duties.

① Gaming and Graphics Rendering
GPUs are important for rendering high-resolution graphics and complicated bodily results in real-time.

② Scientific Computation and Simulation
GPUs facilitate quick computations in advanced simulations for physics, chemistry, and biology.

③ Synthetic Intelligence and Machine Studying
In coaching fashions with giant datasets, GPUs considerably improve pace and effectivity.

④ Video Encoding and Streaming
GPUs rapidly course of and encode video information, offering high-quality streaming.

⑤ Knowledge Evaluation and Large Knowledge Processing
GPUs play a essential function in massive information evaluation, the place large-scale parallel processing is critical.

CPUs have just a few high-performance cores appropriate for sequential duties, whereas GPUs encompass hundreds of cores optimized for parallel duties.

CPUs excel in single-core efficiency and complicated operations, whereas GPUs outperform in duties requiring large-scale parallel processing. GPUs devour extra energy attributable to their many cores however are extra environment friendly for particular duties requiring parallel processing.

CPUs are perfect for working system administration and software execution, whereas GPUs are greatest for graphics processing, machine studying mannequin coaching, and different parallel-intensive duties.

It isn’t potential to make use of a GPU alone. it have to be used together with a CPU. It’s because a GPU can’t independently handle and function a system. The mixture of CPU and GPU maximizes their respective strengths, permitting high-performance computing duties to be carried out effectively.

GPUs are optimized for dealing with extremely parallelized duties, however they can’t independently run an working system or handle a system. For that reason, GPUs perform as auxiliary processing models to the CPU.

Moreover, working methods and most software program are designed to run on the CPU. The CPU handles the mandatory serial computations required for working system and system administration duties. Whereas GPUs can carry out large-scale parallel computations rapidly, they can’t handle system duties resembling job scheduling, reminiscence administration, and enter/output processing. These duties are dealt with by the CPU. GPUs are usually managed by way of drivers and APIs that run on the CPU. For instance, CUDA drivers and libraries run on the CPU, which manages and executes GPU duties.

Ex1) Graphics Rendering Program
This instance demonstrates a fundamental graphics rendering program utilizing Pygame and OpenGL in Python. Pygame is a library used for growing video video games, and OpenGL is a cross-language, cross-platform API for rendering 2D and 3D vector graphics.

This easy instance units up the essential construction wanted to render graphics utilizing OpenGL in a Pygame window, laying the groundwork for extra advanced graphics rendering duties.

import pygame
from OpenGL.GL import *
from OpenGL.GLU import *
import numpy as np
# Initialization
pygame.init()
show = (800, 600)
pygame.show.set_mode(show, pygame.DOUBLEBUF | pygame.OPENGL)
gluPerspective(45, (show[0] / show[1]), 0.1, 50.0)
glTranslatef(0.0, 0.0, -5)
# Rendering loop
whereas True:
for occasion in pygame.occasion.get():
if occasion.kind == pygame.QUIT:
pygame.stop()
stop()
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)
# OpenGL graphics rendering code
pygame.show.flip()
pygame.time.wait(10)

Within the supplied graphics rendering program instance, the GPU is utilized in the course of the rendering course of managed by OpenGL. Right here’s an in depth breakdown of how the GPU is concerned.

• The pygame.show.set_mode() perform initializes the show and units up an OpenGL context. This context permits OpenGL to speak with the GPU to deal with rendering duties.
• Features like gluPerspective() and glTranslatef() configure the view matrix for 3D rendering, which the GPU makes use of to remodel and render objects within the scene.

In essence, the GPU’s function on this code is to deal with all of the rendering duties managed by OpenGL. When OpenGL features are known as, they ship directions to the GPU, which then processes these directions to render graphics effectively. The precise drawing and rendering processes leverage the GPU’s parallel processing capabilities to deal with advanced calculations and render high-performance graphics.

Ex2) Coaching a Machine Studying Mannequin
Within the second code instance, which entails coaching a machine studying mannequin utilizing TensorFlow and Keras, the GPU is utilized to speed up the computation of the neural community coaching course of.

import tensorflow as tf
from tensorflow.keras import datasets, layers, fashions
# Load and preprocess information
(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
train_images, test_images = train_images / 255.0, test_images / 255.0
# Outline mannequin
mannequin = fashions.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(10)
])
# Compile mannequin
mannequin.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# Prepare mannequin
mannequin.match(train_images, train_labels, epochs=10, 
validation_data=(test_images, test_labels))

Right here’s how the GPU is leveraged on this context:

• TensorFlow is a deep studying framework that may make the most of GPUs to speed up computation. When TensorFlow is put in with GPU help (through packages like tensorflow-gpu), it mechanically detects and makes use of obtainable GPUs for computations.
• Keras is a high-level API constructed on high of TensorFlow, making it simpler to outline and prepare neural networks. When utilizing TensorFlow because the backend, Keras may also leverage GPUs.
• Convolutional layers (Conv2D) and dense layers (Dense) contain a lot of matrix multiplications and different linear algebra operations. These operations are computationally intensive and extremely parallelizable, making them superb for GPU acceleration.
• When these layers are outlined and executed inside TensorFlow, the framework offloads the computation to the GPU, if obtainable. This considerably quickens the coaching course of.
• Throughout the mannequin coaching part (mannequin.match), TensorFlow performs quite a few ahead and backward passes by way of the neural community. Every epoch entails processing all of the coaching information, calculating gradients, and updating the mannequin weights.
• The GPU accelerates these computations by performing many operations in parallel, particularly helpful for big datasets and deep neural networks. This reduces the coaching time in comparison with utilizing a CPU alone.

Ex3) Scientific Simulation
Within the third instance, which entails a scientific simulation utilizing CUDA with the Numba library, the GPU is utilized to speed up vector addition operations. On this instance, the GPU is utilized to carry out the vector addition operation in parallel, considerably accelerating the computation.

The vector_add perform is compiled to run on the GPU, and the parallel nature of the GPU structure permits for environment friendly processing of enormous arrays by distributing the work throughout many threads.

import numpy as np
from numba import cuda
@cuda.jit
def vector_add(a, b, c):
idx = cuda.grid(1)
if idx < a.measurement:
c[idx] = a[idx] + b[idx]
# Initialize information
N = 100000
a = np.ones(N, dtype=np.float32)
b = np.ones(N, dtype=np.float32)
c = np.zeros(N, dtype=np.float32)
# Carry out vector addition on the GPU
threads_per_block = 256
blocks_per_grid = (a.measurement + (threads_per_block - 1)) // threads_per_block
vector_add[blocks_per_grid, threads_per_block](a, b, c)
print(c)

Right here’s a short clarification of how the GPU is leveraged:

• The perform vector_add is adorned with @cuda.jit, indicating that it needs to be compiled for execution on the GPU. Numba interprets this perform into CUDA code, which may run on the GPU.
• The vector_add perform is launched on the GPU with a specified variety of threads per block and blocks per grid. Every thread computes the addition for a single component of the arrays a and b.
• The GPU executes hundreds of threads in parallel, permitting for your entire vector addition operation to be accomplished a lot sooner than it might be on a CPU, particularly for big arrays.

Ex4) Knowledge Evaluation Process
Within the fourth instance, which entails information evaluation utilizing cuDF and cuML libraries, the GPU is utilized to speed up information processing and machine studying duties.

import cudf
import cuml
# Load information
df = cudf.read_csv('information.csv')
# Knowledge preprocessing
df['feature'] = df['feature'].astype('float32')
# KMeans clustering
from cuml.cluster import KMeans
kmeans = KMeans(n_clusters=3)
kmeans.match(df)
print(kmeans.labels_)

Right here’s a short clarification of how the GPU is leveraged:

• The cudf library is used to load and preprocess information. cudf is a GPU-accelerated DataFrame library much like pandas, designed to run on NVIDIA GPUs. Operations resembling studying CSV information and typecasting are executed on the GPU, offering sooner efficiency in comparison with CPU-based operations.
• The cuml library is used for machine studying duties. On this case, cuml.cluster.KMeans performs KMeans clustering. cuML is a set of GPU-accelerated machine studying algorithms that leverage the GPU for sooner computation.
• The match technique computes the clustering on the GPU, profiting from parallel processing capabilities to deal with giant datasets extra effectively than a CPU.

Through the use of cudf and cuml, the instance demonstrates how GPU acceleration can considerably enhance the efficiency of information evaluation and machine studying workflows.