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Artificial intelligence
fromTheregister
1 month ago

Nvidia eyes design, simulation biz with $2B Synopsys stake

Nvidia is investing $2 billion in Synopsys to accelerate GPU-powered simulation, AI tools, and CUDA-X support across semiconductor design, manufacturing, and industrial digital twins.
Artificial intelligence
from24/7 Wall St.
3 months ago

Buy, Sell or Hold: NVIDIA Stock At $195

Nvidia's dominant AI GPU leadership, massive Data Center revenue growth, strong cash position, and developer lock-in support continued gains but valuation prompts profit-taking consideration.
more#nvidia
Artificial intelligence
fromIT Pro
1 month ago

Nvidia buys $2bn worth of Synopsys in "huge deal"

Nvidia bought $2 billion of Synopsys stock and formed a multi-year partnership to accelerate chip-design workflows using GPU-accelerated computing, CUDA, AI physics, and Omniverse integration.
Startup companies
fromBusiness Insider
1 month ago

This startup created a tool to break Nvidia's chip lock-in. Read the pitch deck Spectral Compute used to raise $6 million.

Spectral Compute raised $6 million to build SCALE, a framework that enables CUDA-written applications to run on non-Nvidia GPUs, initially supporting some AMD architectures.
Software development
fromWIRED
3 months ago

A Former Apple Luminary Sets Out to Create the Ultimate GPU Software

Modular provides portable GPU optimization software that can outperform vendor tools, but faces adoption, competition from Nvidia/AMD, and customer concerns over added costs.
#gpu-programming
more#gpu-programming
Artificial intelligence
fromHackernoon
2 years ago

This New Language Might Kill NVIDIA's GPU Monopoly | HackerNoon

The shift from CUDA to heterogeneous computing signals a revolution in software design and deployment, unlocking new potentials in performance and efficiency.
Java
fromInfoQ
7 months ago

Bringing GPU-Level Performance to Enterprise Java: A Practical Guide to CUDA Integration

Java can integrate with CUDA to achieve significant performance gains in workloads.
JNI allows developers to connect Java with native CUDA for high-performance tasks.
Understanding concurrency vs multithreading is essential for utilizing CUDA effectively.
GPU computing offers benefits beyond AI, applicable to secure data processing and more.
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