Why GPU Can Process Image Much Faster than CPU?

February 12, 2021

Graphical Processing Unit (GPU) and Central Processing Unit (CPU) have many commonalities amongst them and, at the same time, have significant differences when it comes to their roles and characteristics. Technical advancements have offered GPU capabilities to compete with the established players like CPUs for making them ideal for a plethora of applications such as fast image processing.

This blog throws light on GPUs' abilities and CPUs for fast image processing and the benefits and reasons why GPUs have the upper hand over CPU-based solutions.

Before going into the detail, let’s understand what CPUs and GPUs are, along with the critical aspects of fast image processing.

What Are CPUs?

CPU is often referred to as the heart or brain of a computer and is responsible for running most of the software. Simultaneously, specific applications, such as image processing, can be overwhelming for a CPU to manage. A GPU is designed to take care of such applications.

What Are GPUs?

A GPU is specially designed for tasks like quick image rendering. This specialized type of microprocessors can respond to graphically intense applications that can drain the CPU and degrade its performance. Although initially designed to offload image processing related tasks from CPUs, modern technology has offered today’s GPUs the capability to perform rapid mathematical operations for many other applications besides rendering.

Vital Aspects of Fast Image Processing Algorithms

Fast image processing algorithms have specific vital characteristics such as parallelization, locality, simplicity, and how they help GPUs offer superior performance than CPUs.

  • Parallelization Potential – Tasks can be processed in parallel as every pixel doesn’t depend on the information from other processed pixels.
  • Locality - Each pixel's position is determined based on the positions of a limited number of neighboring pixels.
  • 16/32-bit precision arithmetic – In general, a 16-bit integer data type is adequate for storage, and 32-bit floating-point arithmetic is sufficient for image processing.

Following are specific aspects essential for fast image processing.

  • Superior Image Processing Quality – Quality is critical in fast image processing. You can use various algorithms to accomplish the same image processing operation to get varying output quality and resource intensity. Resource-intensive algorithms using multilevel optimization can give you the necessary performance benefits and provide the output within a reasonable time compared to the fast but crude algorithms.
  • Maximum Performance – For maximizing “fast image processing” performance, you can either optimize software code or increase hardware resources such as the number of processors. When it comes to the price-to-performance ratio, a GPU outpaces a CPU, and you can reap its full potential using multilevel algorithm optimization and parallelization.
  • Reduced Latency – A GPU offers reduced latency as it takes less time to process an image due to its inherent parallel pixel processing architecture. On the other hand, a CPU provides modest latency as the parallelism is implemented at image lines, tiles, and frame level.

How GPU differs from CPU?

A range of differences makes GPUs superior to CPUs when it comes to fast image processing.

Cores 

While a CPU contains minute powerful cores, a GPU has hundreds of thousands of weak and smaller cores.

Number of Threads 

A CPU architecture allows each physical CPU core to execute two threads on two virtual cores such that an individual thread executes the instructions independently. On the other hand, a GPU uses single instruction, multiple threads (SIMT) architecture, where 32 (generally) threads work on the same instruction as against a single thread in a CPU.

Type of Processing 

Due to its architecture, a CPU is ideal for serial instruction processing, while a GPU is designed for parallel instruction processing.

Thread Implementation 

Using actual genuine thread rotation, a GPU launches instructions every time from different threads. With a parallel algorithm and high load, it proves to be more efficient as a hardware implementation and is ideal for implementing several image processing algorithms. Unlike a GPU, a CPU uses out-of-order execution.

Why is GPU Superior to CPU?

Speed 

Due to its parallel processing capability, a GPU is much faster than a CPU. For the hardware with the same production year, GPU peak performance can be ten-fold with significantly higher memory system bandwidth than a CPU. Further, GPUs provide superior processing power and memory bandwidth. They are up to 100 times faster than CPUs with non-optimized software without AVX2 instructions while performing tasks requiring large caches of data and multiple parallel computations.

Managing Load 

Unlike a CPU, a GPU can reduce memory subsystem load by dynamically changing the number of available registers (from 64 to 256 per thread).

Simultaneous Execution of Several Tasks 

Several hardware modules of GPU enable concurrent execution of entirely different tasks. For example, Asynchronous copy from and to GPU, image processing on Jetson, tensor kernels for neural networks, video decoding, and encoding, computations on GPU, DirectX, OpenGL, and Vulkan for rendering.

Shared Memory 

All modern GPUs come with shared memory, which is several times faster than the bandwidth of a CPU’s L1 cache. It’s designed explicitly for algorithms with a high degree of locality.

Embedded Applications 

GPUs offer significantly greater flexibility and a practical alternative for specialized embedded applications, including FPGAs (Field-Programmable Gate Arrays) and ASICs (Application-Specific Integrated Circuits).

Some Myths Related to GPUs

Overclocking can damage your card.

Overclocking may cause a reset of settings (mostly CPU), inconsistent behavior, or crash without any actual damage to the video card. Though heat and voltage can impact the card, modern GPUs are smart enough to either shut down or throttle to prevent damage.

Merely 96 kB of shared memory capacity for each multiprocessor.

If managed efficiently, 96 kB memory size is adequate for each multiprocessor.

Back and forth data copying to CPU can downgrade the performance.

It’s a myth. As the best solution, you can perform all processing on the GPU within a single task. You can copy the source data either once or asynchronously to the GPU and return the computation results to the CPU at the end.

Summary

To sum it up,

  • GPUs serve as an excellent solution for fast and complex image processing tasks and outperform CPUs significantly.
  • GPU’s parallel processing architecture results in processing time reduction for a single image.
  • High GPU performance software can offer high energy efficiency, lower hardware cost, and lower cost of ownership.
  • Further, GPU provides low power consumption, high performance, and flexibility for embedded and mobile applications and compete with highly specialized ASIC/FPGA solutions.

For more blogs on data science and cloud computing, checkout E2E Networks website. Also if you are interested in taking a GPU server trial feel free to reach out to me @ 7795560646.

Latest Blogs
This is a decorative image for: Top 12 skills a CEO should demand in a data scientist to hire in 2022
September 21, 2022

Top 12 skills a CEO should demand in a data scientist to hire in 2022

Two decades ago, data scientists didn’t exist. Sure, some people cleaned, organized and analyzed information — but the data science professionals we admire today stand at the head of a relatively new (and vaunted) career path.

It is certainly one of the most popular careers because it is in great demand and highly paid. With data being the primary fuel of industry and organization, company executives must now determine how to drive their company in this rapidly changing environment. Not only is a growth blueprint essential, but so are individuals who can put the blueprint into action. When most senior executives or human resource professionals think of data-driven employment, a data scientist is the first position that comes to mind.

In this blog, we will discuss the top 12 skills a CEO should demand if hiring a data scientist in 2022. 

  1. Problem-Solving and Critical Thinking

Finding a needle in a haystack is the goal of data science. You'll need a candidate who has a sharp problem-solving mind to figure out what goes where and why, and how it all works together. Thinking critically implies making well-informed, suitable judgments based on evidence and facts. That means leaving your own ideas at the door and putting your faith - within reason - in the evidence. 

Being objective in the analysis is more difficult than it appears at first. One is not born with the ability to think critically. It's a talent that, like any other, can be learned and mastered with time. Always look for a candidate who is prepared to ask questions and change his/her opinion, even if it means starting over.

  1. Teamwork 

If you go through job listings on sites like Indeed or LinkedIn, you'll notice one phrase that appears repeatedly: must work well in a team. Contrary to popular belief, most scientific communities, including those in data science, do not rely on a single exceptional mind to drive forward development. A team's cohesiveness and collaboration power are typically more significant than any one member's brilliance or originality. Your potential candidate will not contribute to success if s/he does not play well with others or believes that s/he does not require assistance from your colleagues. If anything, candidates' poisonous attitudes may cause stress, decreased levels of accomplishment, and failure on the team.

Harvard researchers revealed in 2015 that even "moderate" amounts of toxic employee conduct might increase attrition, lower employee morale, and reduce team effectiveness. Eighty percent of employees polled said they wasted time worrying about coworker incivility. Seventy-eight per cent claimed toxicity had reduced their dedication to their work, and 66 per cent said their performance had suffered as a result. The fact is that being a team player is significantly more productive and fulfilling than being a solo act. Look for a candidate with good cooperation abilities, and both you and your team will profit!

  1. Communication 

Capable data scientists must be able to communicate the conclusions they get from data. If your candidate lacks the ability to convert technical jargon into plain English, no matter how significant the results are, your audience will not grasp them. Communication is one of the most important skills a data scientist can learn — and one that many pros struggle with. 

One 2017 poll that tried to uncover the most common impediments that data scientists encountered at work discovered that the majority of them were non-technical. Among the top seven barriers were "explaining data science to others," "lack of management/financial support," and "results not utilised by decision-makers."

You fail if you can't communicate - therefore look for a candidate who knows how to interpret! And can break down complicated topics into digestible explanations; rather than giving a dry report.

  1. Business Intelligence 

Sure, a candidate can’t start teaching abstruse mathematical theory whenever you want — but can they explain how that theory can be applied to advance business? True, data scientists must have a strong grasp of their field as well as a solid foundation of technical abilities. However, if a candidate is required to use those abilities to advance a corporate purpose, they must also have some level of business acumen. Taking a few business classes will not only help them bridge the gap between their data scientist peers and business-minded bosses, but it will also help them advance the company's growth and their career as well. It may also assist them in better applying their technical talents to create useful strategic insights for your firm.

  1. Statistics and mathematics 

When it comes to the role of arithmetic in machine learning, perspectives are mixed. There is no disputing that college-level comprehension is necessary. Linear algebra and calculus should not sound like other languages. However, if you're looking for a candidate for an internship or a junior position, then they don't need to be a math guru. But if you are looking for a candidate to work as a researcher, then the candidate must have more than just a strong math background. After all, research propels the business ahead, and you won't be able to accomplish anything until you have a candidate with a thorough grasp of how things function.

The fact is that just because data science libraries enable data scientists to perform complex arithmetic without breaking a sweat doesn't mean they shouldn't be aware of what's going on behind the surface. Get a candidate with the fundamentals right.

  1. AI and Machine Learning 

Machine learning is an essential ability for any data scientist. It is used to create prediction models ranging from simple linear regression to cutting-edge picture synthesis using generative adversarial networks. When it comes to machine learning, there is a lot to look for in a potential candidate. Regression, decision trees, SVM, Naive Bayes, clustering, and other classic machine learning techniques (supervised and unsupervised) are available. Then there are neural networks, which include feed-forward, convolutional, recurrent, LSTM, GRU, and GAN. There's also reinforcement learning, but you get the idea - machine learning is a vast subject. 

  1. Skills in cloud and MLOps

To remain relevant to the industry's current demands, more than three out of five (61.7%) companies say they need data scientists with updated knowledge in cloud technologies, followed by MLOps (56.1%) and transformers (55%). Three out of every four professionals with ten or more years of experience are learning MLOps to expand their skill sets. Cloud technologies (71.7%) are being learned as a fundamental new talent by mid-career professionals with 3-6 years of experience, followed by MLOps (62.3%), transformers (60.4%), and others.

Professionals in retail, CPG, and e-commerce are more likely (73.7%) to learn cloud technology as a new skill. As much as 70% of BFSI personnel upskill in MLOps. Another 70% and 60% of pharma and health workers are interested in acquiring transformers and computer vision as fundamental skills.

So make sure you don't miss out on such a talent who can bring cloud and MLOps skills into your company. 

  1. Storytelling and Data Visualization 

Data visualisation is enjoyable. Of course, it depends on who you ask, but many people consider it the most gratifying aspect of data science and machine learning. Look for a candidate who is a visualisation specialist and understands how to show data based on business requirements, and also how to integrate visualisations so that they tell a story. It might be as easy as integrating a few plots in a PDF report or as sophisticated as creating an interactive dashboard suited to the client's requirements.

The data visualisation tools utilised are determined by the language. Plotly, which works with R, Python, and JavaScript, may be the best option if you need a candidate for searching for a cross-platform interactive solution. Consider Tableau and PowerBI when you need a candidate for viewing data using a BI tool. 

Figure: Use of Data Visualization tools. 

  1. Programming 

Without programming, there is no data science. How else would you give the computer instructions? All data scientists must be familiar with writing code, most likely in Python, R, or SQL these days. The breadth of what a candidate will perform with programming languages differs from that of traditional programming professions in that they’ll lean toward specific libraries for data analysis, visualisation, and machine learning. 

Still, thinking like a coder entails more than just understanding how to solve issues. If there is one thing that data science sees a lot of, it is issues that need to be solved. But nothing is worse than understanding how to fix an issue but failing to transform it into long-lasting, production-ready code.

Out of the host of programming languages, 90% CEOs hire data science specialists who are specialists in Python as their preference for statistical modelling. Beyond that, the use of SQL (68.4%) is highest in retail, CPG, and ecommerce, followed by IT at 62.9%. R is the most widely used programming language if you operate in the pharma and healthcare business, with three in five (60%) data scientists reporting using it for statistical modelling.

  1. Mining Social Media 

The process of extracting data from social media sites such as Facebook, Twitter, and Instagram is referred to as social media mining. Skilled data scientists may utilise this data to uncover relevant trends and extract insights that a company can then use to gain a better knowledge of its target audience's preferences and social media actions. You need data scientists well versed with this type of study as it is essential for building a high-level social media marketing plan for businesses. Given the importance of social media in day-to-day business and its long-term viability, hiring data scientists with social media data mining abilities is an excellent strategy for company growth.

  1. Data manipulation 

After collecting data from various sources, a data scientist will almost surely come across some shoddy data that has to be cleaned up. You need to hire a candidate that knows what Data wrangling is. How to use it for the rectification of data faults such as missing information, string formatting, and date formatting. 

  1. Deployment of a Model 

What is the use of a ship if it cannot float? Non-technical users should not be expected to connect to specialised virtual machines or Jupyter notebooks only to check how your model operates. As a result, the ability to deploy a model is frequently required for data scientist employment.

The easiest solution is to establish an API around your model and deploy it as any other application — hosted on a virtual machine operating in the cloud. Things get harder if you wish to deploy models to mobile, as mobile devices are inferior when it comes to hardware. 

If speed is critical, sending an API call and depending on an Internet connection isn't the best option. Consider distributing the model directly to the mobile app. Machine learning developers may not know how to design mobile apps, but they may examine lighter network topologies that will have reduced inference time on lower-end hardware.

Consider hiring a candidate who is well versed with all the things discussed above related to deploying a model. 

Conclusion

And there you have it: the top twelve talents skills a CEO must look for while hiring a data scientist. Keep in mind that skill levels or talents themselves may differ from one firm to the next. Some data science jobs are more focused on databases and programming, while others are more focused on arithmetic. Nonetheless, we believe that these 12 data science skills are essential for your potential candidate in 2022.

This is a decorative image for: Towards Complete Icon Labelling in Mobile Applications
September 21, 2022

Towards Complete Icon Labeling in Mobile Applications

Why is Icon Labeling Important?

Icon labeling projects aim to create a machine learning algorithm that can automatically label icons in mobile applications. The algorithm is generally trained on a dataset of labeled images and learns to recognize the objects in the pictures. Labeling icons is a tedious task and often requires human intervention.

Thus, automating this process by training an algorithm on a labeled image dataset can pave the way for complete icon labeling. This article will walk you through labeling icons using machine learning. Icons may seem like a small part of your app, but they're critical for branding and user experience. Icons need to be labeled by hand, which is time-consuming and tedious.

It isn't easy to keep up with the volume of new icons on mobile phones, and keeping the icons organized takes a lot of effort. The wrong icon can ruin your app's design and make it difficult for users to use. With any icon labeling project, labeling icons is easy. Your database will be automatically and consistently labeled by Artificial Intelligence that recognizes objects in images.

How to Label Icons Effectively?

Prepare your data set. It should include the icon's name, a short description, and an image of the icon. You can use any file type uploaded to any storage or drive.

Next, you will need to create a project in the platform and enable billing if it has not been done already. Then you can create a new dataset by specifying a dataset ID and name.

The use of labeling icons in UI design has been around for many years. The most popular use case is to offer users an indication of what they can do on a particular screen. You can do so by adding labels to the icons.

Icons often indicate the user's action to complete a task (e.g., save, delete, etc.). However, this could be problematic for people with disabilities or who cannot understand or read English fluently due to language and communication barriers.

Labeling icons is complex, especially when the icon is not well-known. We propose a novel method for labeling icons with conversational agents and chatbots. Machine Learning techniques can help generate a set of labeled examples for a conversational agent or chatbot training.

Tips for using icons in your app

Labels are the most critical component of an icon, as they communicate the meaning to users. Designers should keep their icons simple and schematic and include a visible text label to make them good touch targets.

Icon designers also need to be careful when designing icons. Designers should keep their icons simple and schematic, include a visible text label and make them good touch targets. Labels are the most crucial component of an icon as they communicate meaning to users.

Icons should be simple and schematic with a clear visible text label that communicates what the icon means to users. Icons are also suitable for touching targets for screen readers, so designers must consider this when designing them.

Icon labels are an essential feature that can make or break an icon. Designers are often designing icons with less-than-perfect or downright nasty labels. Terrible labels can lead to misinterpretation and confusion, leading to lost business or a tarnished reputation. Labels are not just crucial for designers; they're critical to users.

The label conveys the meaning of a symbol, so it should be simple, visible, and easy for interaction purposes. If designers ignore these principles, icons will become meaningless, unhelpful, and challenging to navigate. Designers must create good touch targets that are easily recognizable. After all, it's about bringing users the best.

Conclusion

Iconography is the basis of every UI design. Designers need to understand how it shapes an interface’s usability. Every icon in an interface serves a purpose. When implemented carefully and in the correct manner, icons can help users navigate through the workflow. It's good to be a part of this cutting-edge iconography which can help you further push the boundaries of Deep Learning and expand your understanding of recognizing icon types.

This is a decorative image for: Prompt-based Learning can Make Language Models More Capable
September 21, 2022

Prompt-based Learning can Make Language Models More Capable

Artificial Intelligence promotes supervised learning and builds a system that catches the fundamental relationships between various inputs and outputs. In this way, Natural Language Processing (NLP) plays a very integral part in linking the input with the output. It was hard to retrieve the knowledge and information from the important datasets in the NLP technology. But with the development of neural network models, it has become very convenient to retrieve information from the datasets. 

Learn the neural network models to better understand and deal with the features of NLP technology. The new training that the researchers are trying to provide to the learners of neural networks to understand NLP models is popularly known as Prompt-based Learning. This means this type of learning will not be supervised anymore. It will however be used to solve various tasks but what is the correct prompt for a particular language model is a task in itself.

What is Prompt-based Learning?

Machine Learning researchers and engineers need a proper strategy through which they can train the large language models. Such training will enable the large language model (LLM) to deal with different tasks without the need for a training session each time. 

There are certain traditional languages which are being popularly used but they are required to be retrained every time there is a change in the functioning. BERT and GPT-3 are the two best examples of such languages. Search languages need to be fine-tuned every time. As a result, a lot of tasks have to be done as retraining to get the work done. Prompt-based learning eliminates all these needs.

Advantages of Prompt-based Learning

Prompt-based learning offers several benefits when compared to the traditional way of fine-tuning methods. The benefits of prompt-based learning are enumerated as follows:

  • Prompt-based learning works exceptionally well in the case of labelled data that are in small amounts.
  • It helps you to achieve strong and accurate results by doing comparatively less work.
  • Prompt-based learning also helps you to achieve the standard of efficiency and the process becomes cost-effective.
  • These AI models require less energy consumption and thus, Prompt-based learning saves energy consumption.

These advantages play a very big part in why various companies choose prompt-based learning to train their NLP technologies.

Challenges of Prompt-based Learning

Despite a ton of advantages that prompt-based learning has in store, it has certain disadvantages as well. These disadvantages come in the form of challenges that can be stated as follows:

  • It is difficult to design prompt designs that will be effective.
  • Finding an accurate combination of prompt templates and the answers to them is another difficult task at hand.
  • Prompt-based learning has been implied in a few selected domains only. It needs to be explored more.
  • If the person who is handling the AI models does not understand the proper working of Prompt-based learning, then all the hard work will go in vain.
  • A constant trial and error approach is required to keep a check on whether a particular strategy is yielding fruitful results or not.

The discipline of Artificial Intelligence is ever-changing and ever-growing. It is one of the most dynamic disciplines that have come into existence. Also, know about why good conversation matters and how AI can help so that you get deeply rooted in this idea. Prompt-based learning is breezing the gap between the traditional and new age data models.

Build on the most powerful infrastructure cloud

A vector illustration of a tech city using latest cloud technologies & infrastructure