It’s all about GPUs — Accelerating ML executions

The evolution from CPU to GPU computing for machine learning represents one of the significant paradigm shifts in computing history, transforming how we approach complex computational problems. This transition from sequential to parallel processing has enabled breakthroughs in artificial intelligence that were previously considered impractical or cumbersome.

Today’s state-of-the-art AI systems combine multiple acceleration approaches: multi-GPU/TPU training at scale, mixed precision computation, model parallelism, and distributed training across thousands of processors.

Transformative journey can be broken down into Four Ages of Computational Processing — 

• Age of Sequential Logic (1945–1985)
• Age of Limited Parallelism (1985-2005)
• Age of GPU Computing (2006-2015)
• Age of AI Acceleration (2016-Present)

Age of Sequential Logic (1945-1985) – Early electronic computers operated sequentially, following von Neumann architecture established in 1945 with EDVAC. Implementation has evolved with introductions of both CISC and RISC designs including ARM processors which now dominate mobile computing. Neural networks, though conceptualized in the 1940s had remained theoretical due to computational limitations.

Age of Limited Parallelism (1985-2005) – As sequential computing limitations became apparent, vector processors in supercomputers introduced specialized parallelism, where it remained inaccessible to most users. Intel’s ground work on SIMD (Single Instruction, Multiple Data) techniques like MMX and SSE and inherent adoption of multiple cores with hyper threading gained momentum in parallel processing even though thermal and energy constraints forced CPU manufactures to be more innovative towards power efficiency and cooling solutions leading to low power CPU architectures today.

Age of GPU Computing (2006-2015) – NVIDIA’s introduction of CUDA (i.e. Compute unified Device architecture) in 2006 democratized access to massively parallel computing by repurposing GPUs for general computation. In other words, CUDA is a parallel computing platform and programming model. Nvidia originally developed GPUs for gaming, focusing on rendering complex graphics with high-speed parallel processing. However, the same parallel architecture that made GPUs great for rendering images turned out to be incredibly efficient for AI workloads. The breakthrough came in 2012 when AlexNet, trained on GPUs, dramatically outperformed traditional computer vision approaches in the ImageNet competition.

Age of AI Acceleration (2016-Present) – GPU architectures now incorporate specialized AI components and has been defined by specialized hardware designed explicitly for neural network operations. NVIDIA’s Tensor Cores (in V100, RTX series, A100, H100 architectures), AMD’s Matrix Cores (in MI200, Radeon RX 7000) provide 5-10x speedups for matrix math’s. Meanwhile, custom AI processors including Google’s TPUs, Cerebra’s wafer-scale CS-2, Graphcore’s IPUs, and Apple’s Neural Engine in M-series chips deliver even greater efficiency through dedicated circuitry, mixed precision, and memory architectures optimized specifically for machine learning workloads.

Core Advantages of GPU Acceleration for ML

Architectural Advantage:

CPUs are equipped with a few high-performance cores designed for tasks that require strong sequential processing, such as running operating systems or complex single-threaded applications. In contrast, GPUs have thousands of smaller, simpler cores optimized for performing many operations simultaneously—ideal for tasks like rendering graphics or processing large datasets in parallel.

For instance, a CPU might handle a web server’s request processing efficiently, while a GPU excels at rendering complex 3D graphics in video games or accelerating scientific simulations.

Mathematical Alignment:

Many machine learning tasks, especially training deep neural networks, involve large-scale matrix operations and repetitive calculations across extensive datasets. GPUs are architected to perform these parallel mathematical operations efficiently. For instance, multiplying large matrices during neural network training or performing convolution operations in image processing align naturally with the GPU’s ability to handle thousands of similar calculations at once.

Performance Impact:

Utilizing GPU acceleration can drastically cut down training times—from weeks or days to merely hours or minutes—allowing researchers to iterate rapidly. This acceleration enables the development of more sophisticated models that were previously computationally prohibitive. For instance, training a convolutional neural network for image recognition that once took a week can now be completed in a few hours, facilitating quicker experimentation and improvements.

Democratization Effect:

Thanks to GPU acceleration, powerful AI development tools are now accessible to individual researchers and small organizations, not just large corporations with expensive supercomputers. This has levelled the playing field for innovation. For instance, a small startup or university can train state-of-the-art language models on a high-end GPU workstation, whereas previously such capabilities required massive institutional resources.

Performance Differential: Orders of Magnitude

The performance difference between CPU and GPU for neural network training is dramatic:

Model Type	Dataset Size	CPU Training Time	GPU Training Time	Acceleration Factor
ResNet-50	ImageNet (1.2M images)	~2 months	~14 hours	~100x
BERT-Large	BookCorpus + Wikipedia	~1 year	~4 days	~90x
Transformer	WMT English-German	~45 days	~12 hours	~90x
U-Net	Medical imaging (50k scans)	~3 weeks	~5 hours	~100x

Comparing BERT-Large training times on different hardware setups for BookCorpus + Wikipedia (Numbers are approx.)

Hardware Setup	Training Time
Single CPU (Intel Xeon 8280, 28 cores)	~30 days
Single NVIDIA V100 GPU	~5 days
8 NVIDIA V100 GPUs	~12 hours
1024 NVIDIA V100 GPUs (DGX SuperPOD)	47 minutes
DeepSpeed Optimization (Microsoft, 1024 GPUs)	44 minutes