Abstract: A multi-GPU implementation of the multilevel fast multipole algorithm (MLFMA) based on the hybrid OpenMPCUDA parallel programming model (OpenMP-CUDA-MLFMA) is presented for computing ...

Government-funded academic research on parallel computing, stream processing, real-time shading languages, and programmable graphics processing units (GPUs) directly led to the development of GPU ...

CUDA 13 is a true major release, not just another dot update. It broadens how you write GPU software, tightens the platform and driver story, and cleans up years of incremental decisions made across ...

Rapidly growing protein databases demand faster sensitive search tools. Here the graphics processing unit (GPU)-accelerated MMseqs2 delivers 6× faster single-protein searches than CPU methods on 2 × ...

Nvidia was founded by three chip designers (including Jensen Huang, who became CEO) in 1993. By 1997 they had brought a successful high-performance 3D graphics processor to market; two years later the ...

When it comes to GPU programming, CUDA and SYCL are two prominent frameworks, each with its own strengths and use cases. In this post, I’ll compare CUDA with SYCL, discuss why SYCL was developed, and ...

Explore Bend, a new programming language simplifying GPU development with Python-like syntax. Utilize Bend for automatic parallel code execution, eliminating the need for explicit parallel annotations ...

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous vehicles, and exascale computing.

Refactoring tools, whether fully automated or semi-automated, are essential components of the software development life cycle. As software libraries and frameworks evolve over time, it’s crucial for ...

GPUs have far more cores than CPUs, allowing for a large number of parallel processes. IT engineer Rijul Rajesh has compiled the knowledge needed to take advantage of GPU performance in a blog post.