Graphics Processing Units (GPUs) are now pivotal in high-performance computing, offering substantial computational throughput through inherently parallel architectures. Modern research in GPU ...

The future of computing has arrived in a flash, literally. In A Nutshell Researchers created a computer that performs complex ...

CUDA enables faster AI processing by allowing simultaneous calculations, giving Nvidia a market lead. Nvidia's CUDA platform is the foundation of many GPU-accelerated applications, attracting ...

NVIDIA’s CUDA is a general purpose parallel computing platform and programming model that accelerates deep learning and other compute-intensive apps by taking advantage of the parallel processing ...

Your x86 clusters are obsolete, metadata is eating 20% of I/O, and every idle GPU second burns cash The supercomputing ...

A Graphics Processing Unit or GPU in short is used as a co-processor along with the CPU for heavy tasks and computing. Generally, GPUs are used to accelerate or speed up memory-intensive tasks such as ...

One of the key trends for Nvidia right now is the growth of CUDA (Compute Unified Device Architecture), Nvidia's programming language for general-purpose GPU computing. In a keynote speech, Huang ...

IBM's latest offering of serverless cloud may be perfect for some enterprises wanting to offload GPU or HPC workloads to the ...

Julia is a high-level programming language for mathematical computing that is as easy to use as Python, but as fast as C. The language has been created with performance in mind, and combines careful ...

With this launch, the Duality Platform now supports GPU-backed LLM inference and encrypted Retrieval-Augmented Generation (RAG) within trusted execution environments (TEEs) - a significant performance ...