The new release includes several enhancements to the Math Libraries and improvements for C++ programming.
]]>The new release includes several new features including improved stdpar programming and Arm processor support.
]]>AI is augmenting high-performance computing (HPC) with novel approaches to data processing, simulation, and modeling. Because of the computational requirements of these new AI workloads, HPC is scaling up at a rapid pace. To enable applications to scale to multi-GPU and multi-node platforms, HPC tools and libraries must support that growth. NVIDIA provides a comprehensive ecosystem of…
]]>High-performance computing (HPC) powers applications in simulation and modeling, healthcare and life sciences, industry and engineering, and more. In the modern data center, HPC synergizes with AI, harnessing data in transformative new ways. The performance and throughput demands of next-generation HPC applications call for an accelerated computing platform that can handle diverse workloads…
]]>The new hardware developments in NVIDIA Grace Hopper Superchip systems enable some dramatic changes to the way developers approach GPU programming. Most notably, the bidirectional, high-bandwidth, and cache-coherent connection between CPU and GPU memory means that the user can develop their application for both processors while using a single, unified address space.
]]>The latest NVIDIA HPC SDK update expands portability and now supports the Arm-based AWS Graviton3 processor. In this post, you learn how to enable Scalable Vector Extension (SVE) auto-vectorization with the NVIDIA compilers to maximize the performance of HPC applications running on the AWS Graviton3 CPU. The NVIDIA HPC SDK includes the proven compilers, libraries…
]]>Fortran developers have long been able to accelerate their programs using CUDA Fortran or OpenACC. For more up-to-date information, please read Using Fortran Standard Parallel Programming for GPU Acceleration, which aims to instruct developers on the advantages of using parallelism in standard languages for accelerated computing. Now with the latest 20.11 release of the NVIDIA HPC SDK…
]]>Historically, accelerating your C++ code with GPUs has not been possible in Standard C++ without using language extensions or additional libraries: In many cases, the results of these ports are worth the effort. But what if you could get the same effect without that cost? What if you could take your Standard C++ code and accelerate on a GPU? Now you can!
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