Parallelism: It's About Time

NOTE: The above animation demonstrates, the difference between serial and parallel processing. Play it on repeat if you have to :)

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GPUs are just beginning.

Over the past couple of years, I’ve been noticing something interesting lately in the compute space.

We’re beginning to see a paradigm shift in the way computation is being harnessed.

It’s changing the way startups (and enterprises) are developing products from AI to crypto mining, data processing, and even encryption.

At the core of this transformation is parallelism—a computing approach that allows multiple tasks to be handled simultaneously.

Graphics Processing Units (GPUs) have been the poster child for this shift for a long time (especially in the graphics space), demonstrating how parallel processing can tackle computationally heavy workloads far more efficiently than traditional CPUs.

Today’s piece is going to explore the big key differences between traditional processors and parallel compute architectures, the big shift between these two types of computing approaches, and why this is such a HUGE OPPORTUNITY for startups and developers!

The Power of Parallelism: Why GPUs Changed the Game

We all know NVIDIA has been a huge driver of parallel compute for a very long time - largely backed by their core investment in GPU technology.

Side note: It’s pretty ironic that they’re still called “Graphic” Processing units, even though they have so many broader applications nowadays. Anyway, I digress.

GPUs showed the world that parallelism is a key advantage when it comes to handling complex, data-heavy tasks.

Unlike traditional CPUs, which process tasks sequentially, parallel processors can perform many operations at once. This makes them ideal for applications like:

1. AI & Machine Learning: Training models involves crunching massive datasets—parallelism speeds this up dramatically.

2. Crypto Mining: Solving the mathematical puzzles required for mining cryptocurrencies thrives on parallel compute power.

3. Graphics Rendering: This was the OG. GPUs were built for this, turning millions of pixels into incredible visuals in realtime.

4. Encryption and Decryption: Securing data or breaking codes benefits from the ability to process multiple calculations simultaneously.

These use cases highlight why parallelism has become really powerful—it’s faster, more efficient, and increasingly necessary as computation demands grow.

But…GPUs are just the start of all of this.

Other parallel compute architectures like Field-Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs) are gaining really strong traction, offering even more specialized and efficient solutions.

Don’t worry, we’ll do a quick 101 explaining these two…

ASIC vs. FPGA

ASIC

ASICs are designed from the ground up for one specific task (application) and nothing else — it’s a custom-built chip, designed to perform a single function as efficiently as possible.

Once an ASIC is built, its function is locked in—it can’t be changed or reprogrammed.

Google’s Tensor Processing Unit (TPU) is a prime example of this, where they custom-built their own ASIC processor to help with machine learning tasks, particularly in AI learning and inference. These TPUs help power services like Google Translate and image recognition — tasks that would painfully slow on traditional CPUs.

This makes ASICs:

• Extremely fast and efficient for their specific task.

• Have really good low power consumption compared to more general-purpose processors doing the same job.

BUT, they’re also:

• Expensive to design and manufacture—creating a custom chip requires significant upfront investment.

• Zero flexibility—if the task changes or a better method is found, the ASIC becomes obsolete.

FPGA

An FPGA, on the other hand, is like a Swiss Army Knife—they’re more versatile and adaptable.

Unlike ASICs, FPGAs can be reprogrammed and reconfigured to perform different tasks over time. This makes them ideal for situations where flexibility is key, such as prototyping new technologies or adapting to evolving algorithms.

The below diagram shows that FPGAs arranged into a grid-like form which allows them to be configured/programmed in specific ways.

FPGA Layout / source: Logic Fruit

A good example is in self-driving cars, where FPGAs can be updated as new AI models or sensor technologies emerge.

This makes FPGAs highly flexible—you can reprogram them for different tasks without needing new hardware. And have lower upfront cost compared to designing an ASIC, making them accessible for experimentation.

The downside is that they’re less optimized than ASICs for any single task, meaning they might be slower or less power-efficient. They’re also more complex to program, requiring specialized knowledge to get the most out of them.

In general, it used to be really cost-prohibitive to leverage these two technologies, and while they’re still quite expensive, the economics are starting to make more sense every day.

This shift is opening up a new world of possibilities for startups and established companies, enabling them to build products and services that were once out of reach

From Expensive to Affordable

Parallel compute used to be a luxury only the biggest players could afford.

High-end GPUs, FPGAs, and ASICs came with steep price tags, requiring not just expensive hardware but also specialized expertise to use them. But, costs are coming down thanks to:

• Manufacturing: Improved chip production is lowering the cost of powerful processors.

• Competition: New entrants are challenging established giants, pushing prices lower.

• Cloud Access: Renting parallel compute power in the cloud eliminates the need for huge upfront investments. Just look at how Amazon (AWS) and others and allowing their customers to lease parallel compute infrastructure.

The economics make much more sense now, and that’s unlocking opportunities for a broader range of users.

The Old World: CPUs Ruled

In the old world, startups and companies built their products and services on general-purpose CPUs.

This approach had its gains:

It made it super flexible for users to use CPUs. They could handle anything from web servers to data processing. It was also affordable and easy to program with widely available tools. Their simplicity made it such that there was no need for specialized hardware or expertise.

But as demands grew—especially in AI and machine learning—CPUs hit a wall.

Their sequential nature couldn’t keep up with workloads that just performed better for parallelism, leading to inefficiencies and higher costs over time.

The New World: Parallel Compute

Now, with everything we’re seeing, it’s starting to make sense that we’re more open to parallel compute architectures.

This is such a seminal moment because we now have the technology and economic incentives for this to work in our favor. This opens up huge opportunities for existing and new players to play in this space. Companies are moving to GPUs, FPGAs, and ASICs to power the next wave of products and services.

Microsoft, is starting to realize this more than anyone else.

Historically reliant on CPUs, Microsoft shifted to NVIDIA GPUs to enhance its AI capabilities. In 2017, they began using GPUs to power Bing’s intelligent search and Azure’s machine learning services.

The result?

Faster model training, better search accuracy, and improved scalability—all at a lower operational cost than expanding CPU infrastructure.

Startups Take on NVIDIA

We’re also now witnessing a new wave of companies, challenging giants like NVIDIA in the AI space.

One interesting company is called D-Matrix. They’re developing ASIC-based chips that aim to outpace GPUs in efficiency and cost for certain workloads, especially in the AI inference space.

The fact that new businesses like these have the resources to create this level of competition against the incumbents is a complete game-changer—it drives innovation, accelerates development, and pushes compute costs even lower.

As more players enter the fray, parallel compute becomes less of a luxury and more of a commodity.

Democratizing Parallelism

This cost reduction and rising competition will allow certain types of products (and companies) to take advantage of parallel compute, democratizing an area once reserved for companies with deep pockets.

Startups can now build AI tools, real-time analytics platforms, or blockchain solutions without breaking the bank.

Industries like healthcare (think rapid diagnostics), finance (high-frequency trading), and automotive (autonomous driving) can leverage these tools for specialized, high-performance applications.

Cloud providers offering GPU and FPGA instances further level the playing field, giving developers worldwide access to cutting-edge compute power.

The Future is Hybrid

I don’t think CPUs will disappear anytime soon.

There will always be market for what these chips do, but we should totally recognize that GPUs and other parallel processors (ASICs and FPGAs) are definitely providing more opportunity than we could ever hope for in specific areas.

I believe the future will see more of a healthy mix between these two types.

It’s a strong signal to the market that it pays to not only understand how parallel compute works, but harness it in a way that brings about huge change and innovations, both technologically and economically.

It’s such a huge opportunity, and those who can leverage this technology will be the ones who will be holding the cards.

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Barry.