In the ever-evolving landscape of technology, the semiconductor sector plays a pivotal role in shaping the performance and capabilities of computing devices. Among the key components influencing these advancements are central processing units (CPUs) and graphics processing units (GPUs). This article provides an investment analyst’s perspective on the GPU versus CPU dynamics within the semiconductor sector, from their similarities, differences, to their respective strategic applications.
ACPUs vs GPUs
CPUs, often regarded as the “brain” of computing devices, execute instructions in a serial manner, fetching, decoding, and executing tasks linearly. Their key features include cores, simultaneous multithreading, cache, memory management unit (MMU), and a control unit, all contributing to their efficient functioning.
In contrast, a GPU, also known as a graphics card, specialises in rendering images and videos. Originally tasked with graphics rendering in gaming and animation, GPUs have since evolved and expanded their capabilities to handle parallel processing tasks efficiently. Equipped with multiple cores, GPUs can significantly outperform CPUs in scenarios where parallel execution is key.
Similarities and differences
Both CPUs and GPUs serve as the foundational engines of computing devices, housing cores, memory, and control units. The core functionalities, such as fetching, decoding, and executing instructions, are fundamental to both units. Modern iterations of CPUs and GPUs often feature multi-core architectures, reflecting the industry’s trend toward enhanced processing power.
While CPUs and GPUs share basic foundational components, their design and purpose differ significantly. GPUs excel in parallel processing, featuring numerous cores or arithmetic logic units (ALU) that handle multiple tasks simultaneously. In contrast, CPUs prioritise serial processing and are optimised for general-purpose computing tasks.
See also: Asian REITs – A good addition to your investment portfolio in 2024
The distinction can be likened to the roles in a kitchen: a CPU is similar to a head chef who handles a variety of tasks in sequence, whereas a GPU resembles a team of skilled sous-chefs, each capable of efficiently performing multiple tasks in parallel.
Strategic applications
Understanding the functional differences between GPUs and CPUs is crucial for strategic investments in the semiconductor sector. GPUs find particular significance in applications that demand parallel processing capabilities:
a) Deep learning:
In the field of deep learning, GPU-based servers are vital for high-performance execution of machine learning, neural networks, and deep learning tasks. The parallel processing prowess of GPUs accelerates complex pattern recognition, a key aspect of artificial intelligence. An example of a company investing heavily in deep learning is Alphabet Inc, with exposure through various businesses with Waymo and DeepMind being more prominent names.
See also: Fibonacci retracement: Two practical ways to trade the markets
b) High-performance computing:
Tasks that demand immense computing power, such as geoscientific simulations, financial projections, and data science applications, benefit from GPU-driven systems. The parallel nature of GPUs enhances efficiency in processing large datasets and performing complex calculations. Leading companies in this segment include Microsoft with its Azure offerings; IBM; and Dell Technologies.
c) Autonomous vehicles:
The advancement of advanced driver-assistance systems (ADAS) and autonomous vehicle (AV) technologies requires scalable computing solutions. GPU-based systems are crucial for managing the complex workloads involved in data collection, algorithm development, simulations, and verification. Beyond electric vehicle manufacturers, leading companies investing in R&D for autonomous vehicles include Intel, Huawei, Bosch, Nvidia, and Uber.
Conclusion
As the semiconductor sector continues to evolve, strategic investments in GPU and CPU technologies become crucial for sustained success. Understanding the distinct functionalities of CPUs and GPUs and their roles in emerging fields like deep learning, high-performance computing, and autonomous vehicles equips investors with valuable insights. In this dynamic market, a balanced approach that considers the synergies between these critical components is essential for navigating the ever-changing semiconductor landscape.
Investors who are in tune with rapid technological advancements can gain significant benefits by understanding the market cycles and investment flows into the research and development of GPU and CPU companies. There are multiple growth areas to consider, such as data centres, high-performance chips, and advanced AI models, which are increasingly in demand worldwide and merit closer attention.
Teo Huan Zi is senior equities specialist at Phillip Securities
