Whether you’re a Machine Learning Engineer or a Data Scientist – you cannot deny the significance and pressing necessity for sieving large datasets in training a ML model. But as datasets increase in volume, complexity and cross-relationships, the demand for processing power also surges exponentially. Even the most minute performance bottleneck in ML model training can inordinately delay the project or introduce irredeemable inaccuracies in the prediction/ inference scheme.
Deep Learning (DL), a key subset of ML, employs Artificial Neural Networks (ANNs) to conduct inference studies. It is, therefore, even more inherently dependent on ingesting massive volumes of data to feed the model.
Traditional CPUs cannot handle such massive data workloads, nor can they deliver the computational power for ML model training. As a consequence of not possessing the requisite processing power, often the entire system lags and grinds to a screeching halt.
But with advanced GPUs and their arrays of CUDA cores and Tensor cores, ML Engineers and Data Scientists can easily streamline ML training without any challenge.
This article will give a complete walkthrough of the impact of GPU accelerated analytics platforms on ML. We will also gather insights into the differences between CUDA and Tensor cores and determine which one is the best fit for ML undertakings. But first…
What Are GPUs?
Graphical Processing Units (GPUs) are powerful electronic chips that were developed to render rich 2D/ 3D graphics and animations in immersive video games, movies and other visual media. Now they’re also being employed in AI/ ML systems across the spectrum from speech transcription to self-driving cars.
It is predicted that the global GPU market will grow from USD 25 billion in 2020 to USD 246.5 billion by 2028. Thanks to their ultra-efficient parallel processing capabilities, GPUs have emerged as the best option for massive-scale data processing and ML model training. Vis-a-vis CPUs, GPUs deliver better performance and lower latencies in tasks which are embarrassingly parallel.
GPU manufacturers like Nvidia, AMD and Intel are perennially locked in an innovation race to design the most optimum devices for accurately performing floating-point arithmetic operations, delivering superfast 3D visual processing, and undertaking error-free number crunching, among other functions.
Nvidia has developed CUDA and Tensor core GPUs for general and special purpose processing. In the next section, we will discuss these different types of cores separately and how you can use GPU computing for big data.
What Are CUDA Cores?
Released in 2007, Compute Unified Device Architecture (CUDA) is a proprietary, specially-designed GPU core that can be roughly considered the equivalent of CPU cores. Though less powerful than a CPU core, CUDA cores’ strength lies in their numbers. Most advanced GPUs can have hundreds and even thousands of CUDA cores which can simultaneously undertake calculations on different data sets in parallel. This parallel processing allows massive amounts of data to be handled relatively faster, enabling ML Engineers to develop and tweak algorithms in less time.
Note that each CUDA core is still executing only one operation per clock cycle, the same as a CPU core. But GPUs’ SIMD architecture enables the hundreds/ thousands of CUDA cores to simultaneously address one data point each, thereby accomplishing more in less time.
On the other hand, even the most modern workstation and gaming CPUs come with 16 or 32 or 48 cores.
Since its inception, the CUDA technology has not only revolutionized data analytics, data visualization and database management, but also the AI/ ML development scene. Code can be written in C, C++, Fortran, OpenCL (vendor-independent) and Direct Compute among other languages to allow a CUDA-enabled GPU to take up general purpose computing and data processing.
The CUDA instruction set can also leverage software and programs that provide direct access to virtual instructions in Nvidia GPUs. Furthermore, CUDA-core GPUs also support Graphical APIs such as Direct3D, OpenGL etc., and programming frameworks like OpenCL and OpenMP.
Where Do We Use CUDA Cores?
Enterprises as well as individuals use CUDA cores for real-time computing, compute-intensive 3D-graphics, game development, cryptographic hashing, physics engines, and data science-related operations. CUDA-GPUs are a popular choice for enterprise-grade Machine Learning and Deep Learning operations or training models that consume terabytes of data for training.
For basic neural network training, distributed calculations, accelerated encryption/ decryption, compression, real-time face recognition, and physics simulation, enterprises and engineers prefer GPUs with CUDA cores. They are cost-effective and can deliver high-performance parallel processing. Before the advent of Tensor cores, enterprises extensively used CUDA cores for ML operations.
What Are Tensor Cores?
Tensor cores are specially-designed Nvidia GPU cores that enable dynamic calculations and mixed-precision computing. These cores are powerful enough to accelerate the overall performance while simultaneously preserving accuracy.
The term “Tensor” defines a data type that can hold or represent all forms of data. We can consider it as a container to store multi-dimensional datasets.
Tensor cores leverage fused multiply-addition algorithms. They multiply and add two FP16 and/ or FP32 matrices, thereby significantly speeding up calculations with little or no loss in the ultimate efficacy of the model. While matrix multiplications are logically straightforward, each calculation requires registers and caches where interim calculations can be stored, thus making the entire process computationally very intensive. Hence, Tensor cores are especially well-suited for training humongous ML/ DL models.
There are 4 generations of Tensor cores (3 released and another planned for future release). These are:
- The first generation of Tensor cores used the Volta GPU micro-architecture. These cores trained with mixed precision on FP16 number format. With V100 GPUs’ 640 cores, the first-gen Tensor cores could provide up to 5x increased performance vis-a-vis earlier Pascal-series GPUs.
- The second-generation Tensor cores was introduced with the Turing GPUs that can perform operations 32x faster than Pascal GPUs. These also extended the FP16 calculations to Int8, Int4, and Int1, thereby adding more precision to the computation.
- The third-generation Tensor cores was incorporated in Nvidia Ampere class of GPUs. These further expanded on the potential of Volta and Turing micro-architecture by appending support for bfloat16, TF32, and FP64 precisions. These Tensor-core GPUs can smoothly handle large datasets for ML model training and manifolds accelerate DL-based inference systems.
- Nvidia soon plans to release Hopper microarchitecture-based H100 GPUs, which will be the fourth generation of Tensor GPUs. According to Nvidia press releases, these GPUs can handle FP8 precision format and deliver 30x performance speed-ups over the previous generation.
Difference Between CUDA Cores and Tensor Cores
CUDA or Tensor – Which One to Choose for Training ML Models?
Big data analytics are using GPUs. GPU cores were originally designed to perform graphical computations that involve fewer matrix operations. CUDA cores perform such low-state, single-value multiplication per GPU cycle. This gives CUDA-core GPUs the accuracy and precision to yield better graphics and mathematics-based rendering operations.
Dawn of the era of Data sciences and AI/ ML algorithms led to the emergence of concepts such as DL-based predication, inference, GNN- and GPU-supported large-scale analytics.
With increasing dependence on massive datasets for more accurate model training and inference, CUDA-core GPUs were found middling. Thus, the introduction of Tensor cores by Nvidia. Since ML/ DL and GNN-supported training requires extremely fast processing, Tensor cores excelled by performing multiple operations in one clock cycle. Naturally hence, Tensor cores are better than CUDA cores when it comes to Machine Learning operations.
BERT-LARGE AI Inference Training performance – Nvidia A100 GPU vs. CPU (Source: Nvidia)
In data-driven, AI-centric businesses and Analytics operations, GPUs play a substantial role in developing ML applications. GPUs are exorbitant devices and enterprises should be mindful while choosing to deploy these for their Data science and ML training operations. Availing Cloud GPU services from accredited Cloud Service Providers enables enterprises and individuals to leverage the superb workload acceleration of GPUs at a fraction of the cost when compared to on-premise deployment.
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Cuda Cores Vs. Tensor Cores – FAQs
What are CUDA cores and Tensor cores?
CUDA cores are specialized processors designed for general purpose parallel computing, while Tensor cores are specialized processors designed for deep learning and AI workloads.
What are the main differences between CUDA cores and Tensor cores?
CUDA cores are optimized for a wide range of parallel computing tasks, while Tensor cores are specifically designed to accelerate deep learning and AI workloads, such as matrix operations.
Which one is better for machine learning, CUDA cores or Tensor cores?
It depends on the specific machine learning workload. CUDA cores are better for general purpose parallel computing tasks, while Tensor cores are better for deep learning and AI workloads that involve large matrix operations.
Can I use CUDA cores for deep learning tasks?
Yes, CUDA cores can be used for deep learning tasks, but they may not be as efficient as Tensor cores, which are specifically designed for these types of workloads.
Can I use Tensor cores for general purpose parallel computing tasks?
Tensor cores can be used for general purpose parallel computing tasks, but they may not be as efficient as CUDA cores, which are specifically designed for a wide range of parallel computing tasks.
Are Tensor cores only available on Nvidia GPUs?
Yes, Tensor cores are only available on Nvidia GPUs. CUDA cores, on the other hand, are available on both Nvidia and AMD GPUs.