NVIDIA announces the newest CUDA Toolkit software release, 11.8. This release is focused on enhancing the programming model and CUDA application speedup through new hardware capabilities. New architecture-specific features in NVIDIA Hopper and Ada Lovelace are initially being exposed through libraries and framework enhancements. The full programming model enhancements for the NVIDIA Hopper��
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Back in 2012, NVIDIAN Mark Harris wrote Six Ways to Saxpy, demonstrating how to perform the SAXPY operation on a GPU in multiple ways, using different languages and libraries. Since then, programming paradigms have evolved and so has the NVIDIA HPC SDK. In this post, I demonstrate five ways to implement a simple SAXPY computation using NVIDIA GPUs. Why is this interesting?
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The 11.2 CUDA C++ compiler incorporates features and enhancements aimed at improving developer productivity and the performance of GPU-accelerated applications. The compiler toolchain gets an LLVM upgrade to 7.0, which enables new features and can help improve compiler code generation for NVIDIA GPUs. Link-time optimization (LTO) for device code (also known as device LTO)��
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CUDA 11.2 features the powerful link time optimization (LTO) feature for device code in GPU-accelerated applications. Device LTO brings the performance advantages of device code optimization that were only possible in the whole program compilation mode to the separate compilation mode, which was introduced in CUDA 5.0. Separate compilation mode allows CUDA device kernel code to span across��
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CUDA is the software development platform for building GPU-accelerated applications, providing all the components needed to develop applications targeting every NVIDIA GPU platform for general purpose compute acceleration. The latest CUDA release, CUDA 11.2, is focused on improving the user experience and application performance for CUDA developers. CUDA 11.2��
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Parallel Compiler Assisted Software Testing (PCAST) is a feature available in the NVIDIA HPC Fortran, C++, and C compilers. PCAST has two use cases. The first is testing changes to parts of a program, new compile-time flags, or a port to a new compiler or to a new processor. You might want to test whether a new library gives the same result, or test the safety of adding OpenMP parallelism��
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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��
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Designing new custom hardware accelerators for deep learning is clearly popular, but achieving state-of-the-art performance and efficiency with a new design is a complex and challenging problem. Two years ago, NVIDIA opened the source for the hardware design of the NVIDIA Deep Learning Accelerator (NVDLA) to help advance the adoption of efficient AI inferencing in custom hardware designs.
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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 language design with a sophisticated LLVM-based compiler [Bezanson et al. 2017]. Julia is already well regarded for programming multicore CPUs and large parallel computing systems��
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Cross-platform software development poses a number of challenges to your application��s build process. How do you target multiple platforms without maintaining multiple platform-specific build scripts, projects, or makefiles? What if you need to build CUDA code as part of the process? CMake is an open-source, cross-platform family of tools designed to build, test and package software across��
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Programmability is crucial to accelerated computing, and NVIDIA��s CUDA Toolkit has been critical to the success of GPU computing. Over three million CUDA Toolkits have been downloaded since its first launch. However, there are many scientists and researchers yet to benefit from GPU computing. These scientists have limited time to learn and apply a parallel programming language, and they often have��
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At MapD our goal is to build the world��s fastest big data analytics and visualization platform that enables lag-free interactive exploration of multi-billion row datasets. MapD supports standard SQL queries as well as a visualization API that maps OpenGL primitives onto SQL result sets. Although MapD is fast running on x86-64 CPUs, our real advantage stems from our ability to leverage the��
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The Java ecosystem is the leading enterprise software development platform, with widespread industry support and deployment on platforms like the IBM WebSphere Application Server product family. Java provides a powerful object-oriented programming language with a large developer ecosystem and developer-friendly features like automated memory management, program safety��
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As NVIDIA GPUs evolve to support new features, the instruction set architecture naturally changes. Because applications must run on multiple generations of GPUs, the NVIDIA compiler tool chain supports compiling for multiple architectures in the same application executable or library. CUDA also relies on the PTX virtual GPU ISA to provide forward compatibility, so that already deployed��
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