Multithreaded GPUs

February 11, 2021

With the ever-increasing usage of GPUs in industries for carrying out different applications along with powerful operating systems such as Microsoft Windows 2019, terms such as multiprocessing and multithreading have gained relevance. You can experience traction in your applications besides so many edges when inculcating the same in your GPU-led systems.

Multithreading is interchangeably used in CPUs as well as GPUs to undergo the benefits both offer. While CPU uses a thread-level as well as instruction-level parallelism, GPU employs multithreading when combined in systems. In this article, we will look upon the same concept occurring in GPUs and what advantages can be expected with it along with an example. 

What is Multithreading?

Multithreading, a graphical processing unit (GPU) executes multiple threads in parallel, the operating system supports. The threads share a single or multiple cores, including the graphical units, the graphics processor, and RAM.

Multithreading Overpowering Single Threading

Multithreading uses thread-level parallelism and aims to increase single-core utilization. Due to this, they are combined in systems with many multithreading GPUs and cores.

Multithreading aims in improving the throughput of the tasks happening in the system resulting in better performance. In a thread, if a cache misses an attempt to read or write some data in the cache, resulting in a longer latency, the other threads can handle the task using unused computing resources, leading to faster overall execution, making a shift from the idle state.

Multithreading in GPUs

Now that we have briefed the idea of multithreading, it’s time for the crux of the article where we will explain how multithreading is done in the graphics processing unit of a system.

‘A minimum 4 billion GPUs scheduled simultaneously of which more than 10,000 can run concurrently.’

Threads Running GPU

This exclaims that a GPU can handle so many threads than the available number of cores. Here are some reasons how GPU handles so many threads:

1. Data-Parallelism

There are 4 to 10 threads per core on the GPU. GPU follows Data-parallelism and applies the same operation to multiple data items (single instruction, multiple data {SIMD}). GPU cards are primarily designed for fine-grained, data-parallel computation. The input data process the al­gorithm.

Data Parallelism in GPU

In GPUs, the initialization, synchronization, and aggregation are serial codes smaller than par­allel code. GPU hardware can optimize such load. It combines multiple parallel-processing elements, having a small amount of local memory.

For instance, the Nvidia Geforce GTX 480 graphics card supports 1,536 GPU threads on each of its 15 computing units offering an operational capacity of running 23,040 execution streams. 

2. OpenCL Execution

Computer applications can access GPU resources using a control API in the user libraries and the graphics card driver. The developers can write high-level code to the card through this API. The two major GPU vendors, Nvidia and AMD had their API definitions. CUDA is still the most famous which uses the Nvidia-specific programming model and driver API combination.

GPU-oriented companies recently defined the Open Computing Language (OpenCL), for accessing compute resources. OpenCL specifies the C++ programming model and the control API by the driver for major operating systems and recent GPU hardware.

OpenCL Execution on GPU

OpenCL’s lower-level terminology includes code instances known as kernels. Each processing element has a large register set and some private memory. All kernels or work items from a workgroup share a common global memory accessed by CPU and GPU code. The GPU runtime environment informs each kernel about the range of data items it is processing on execution.

3. Resources Sharing

Threads share the memory and resources of the processes such as message passing and message passing to which they belong. Sharing code and data allows an application to have several activity threads in the same address space. With multithreading, it is possible to run a system program even if a part of it is blocked enhancing the user’s responsiveness.

GPU Sharing Resources

With threads running parallel on multiple processors, different smaller tasks can be performed simultaneously. Allocating memory and resources is a costly affair when time and space are considered. Threads share the memory with their process which makes it economical to create switch threads.

Evolution of Multithreaded Systems

  • During the 1950s, multithreaded processors such as  NBS SEAC and DYSEAC were introduced in 1950 and 1954 respectively. In addition to these, Lincoln Labs TX-2, Bull Gamma 60 and Honeywell 800 in the late 1950s.
  • The 1960s saw the creation of  CDC 6600  and  IBM ACS-360. 
  • The 1970s was the year when HEP (1978) and Xerox Alto (1979) came.
  • 1980s evolved with the introduction of HEP-2 and HEP-3 designs, Transputer, Horizon (1988) - Burton Smith and Stellar GS-1000 as four-way multithreading.
  • During the 1990s and 2000s several upgraded GPU versions such as  Cray/Tera MTA-2 (CMOS)   and the latest Intel Pentium 4 HT came into force which has been the most efficient technology to date.

E2E Networks has been offering the two powerful graphics cards, NVIDIA A30 and NVIDIA A100 with unbeatable performance, power, and memory in the former and the Universal System for AI Infrastructure enabling enterprises to consolidate training, inference, and analytics in the latter.

Examples of Multithreading

Online Shopping:
Multi-threading is incorporated in online stores. Whenever a user browses the available products, reads reviews, places items in the cart, and pays for the products with other people shopping at the site simultaneously with keeping the payment information private with multithreading.

Multithreading offers several advantages to the users and businesses for developing applications. Multithreaded GPUs can be benefited from multiple processors for better performance with executing tasks concurrently. All the tasks can be resolved quickly. Multithread allows a system to achieve better responsiveness, reduce blocking and gain better performance.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

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June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.


The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.


In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.


Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:


Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 


As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online


As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 


Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.



Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 


An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.


In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.




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June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

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