NVIDIA GPU Computing For Chat Conversation Analysis
Chat conversation analysis requires a lot of processing power. GPU computing, or using a GPU (Graphic Processing Unit) as a co-processor to accelerate CPUs (Central Processing Units), can facilitate chat conversation analysis in addition to general-purpose, scientific, and engineering computing applications. The GPU accelerates applications running on the CPU by offloading some of the compute-intensive and time consuming portions of the code. The rest of the application still runs on a CPU. From a user’s perspective, the application runs faster because it is using the massively parallel processing power of the GPU to boost performance. This is often referred to as “heterogeneous” or “hybrid” computing.
A CPU consists of four to eight cores, while a GPU consists of hundreds of smaller cores. Together they operate to crunch through the massive data in a chat application. The high compute performance of a GPU is due to its massive architecture.
There are a number of GPU-accelerated applications that provide easy access to high performance computing (HPC) and can be used effectively for live chat conversation analysis. NVIDIA created CUDA (Compute Unified Device Architecture), a parallel computing platform and programming model that is implemented by their GPUs. The basic idea of CUDA is to use a GPU for parallel programming, and in so doing, to provide better performance for solving complex problems. Rather than use the GPU for gaming, CUDA uses the GPU for processing. By running programs inside the GPU, CUDA utilizes the GPU as a CPU with more cores. CUDA’s primary use is to allow the GPU to be used for more general purpose tasks than graphics display, such as encoding videos, or hardware acceleration of video decoding.
CUDA brings together:
- Massively parallel hardware designed to run generic (non-graphic) code, with appropriate drivers for doing so.
- A programming language based on C for programming the hardware and an assembly language that other programming languages can use as a target.
- A software development kit that includes libraries; various debugging, profiling and compiling tools; and bindings that let CPU-side programming languages invoke GPU-side code.
The point of CUDA is to write code that can run on compatible massively parallel SIMD (Single Instruction, Multiple Data) architectures. This includes several GPU types as well as non-GPU hardware such as NVIDIA Tesla. Massively parallel hardware can run a significantly larger number of operations per second than the CPU at a similar financial cost yielding performance improvements of 50× or more when analyzing live chat data. One of the benefits of CUDA over earlier methods of high-performance computing is that a general-purpose language is available. Instead of having to use pixel and vertex shaders to emulate general-purpose computers, the language is based on C with a few additional keywords and concepts. This makes it a fairly easy language for non-GPU programmers to pick up.
NVIDIA recently took a giant step toward an accelerator processor architecture customized for artificial intelligence (AI). NVIDIA pioneered the development of artificial neural networks through deep learning with the company’s GPUs and CUDA software platform. NVIDIA accounts for majority of deep learning networks in use today. However, while the GPU has proven very effective in parallel processing since the addition of cores, TIRIAS Research has maintained that even NVIDIA would need to eventually migrate to architectures dedicated to AI while preserving its tools and ecosystem to advance its platforms further. Nvidia networks are clearly advantageous for processing live chat data and conducting chat conversation analysis that yields actionable insights.
Most recently, NVIDIA developed the next generation GPU architecture called Volta. Although still referred to as a Graphical Processing Unit, Volta is much more. In addition to enhancing the GPU architecture, NVIDIA added 640 new tensor cores capable of processing 4x4x4 matrix multiplies. This provides a specialized math core that works in conjunction with the standard GPU CUDA cores to add additional processing for deep learning environments. It also accelerates the process of inferring a value based on a trained model, making it useful as an inference engine. NVIDIA essentially put accelerator cores in an accelerator.
Google took a similar route when in developing its proprietary Tensor Processing Unit (TPU). With the tensor core capabilities incorporated into the NVIDIA SDK libraries, runtimes like cuDNN (a CUDA based library for Deep Neural Networks), and TensorRT (a high performance neural network interface), developers will be able to take advantage of the increase in performance from the tensor cores in their AI frameworks without rewriting their applications. Combining NVIDIA’s Volta and Google’s TPUs will provide huge computation power and can help in deriving insights from large chat data.
Authored by Tushar Pandit, Data Science Advisor to RapportBoost.AI.
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