The current standard is C++ 20, with C++ 23 coming up. The C++ 17 standard introduced the groundwork for higher-level parallelism features, but true portability coming in future standards. The memory model in C++ 11 focused on concurrent execution across multicore chips, but lacked the hooks for parallel programming. The initial work was around the memory model, which was included in C++ 11, but needed to be advanced back when parallelism and concurrency started taking hold.
"Focusing on the language standards is how we make sure we have true breadth of compilers and platform support for performance model programming," he explained, adding that Nvidia has worked with the community for more than a decade to make low-level changes of languages for parallelism. Of course, Nvidia's own compiler will extract best performance and value on its GPUs, but it is important to remove the hoops to bring parallelism to language standards, regardless of platform, Costa said. The wild world of non-C operating systems.Hackers weigh in on programming languages of choice.It takes big business to make Nvidia's Omniverse tangible.C: Everyone's favourite programming language isn't a programming language.Standardizing at a language level will make parallel programming more approachable to coders, which could ultimately also boost the adoption open-source parallel programming frameworks like OpenCL, he opined. I think we were arriving at kind of a mecca here of productivity for end users and developers," Costa said. "Then users are of course always able, if they want to, to optimize with a vendor-specific programming model that's tied to the hardware.
As the language is advanced, we arrive at somewhere where we have true open standards with performance portability across platforms," Costa said. "Every institution, every major player, has a C++ and Fortran compiler, so it'd be crazy not to. Nvidia is specifically active in bringing standard vocabulary and framework for asynchrony and parallelism that C++ programmers are demanding. A context might be a CPU thread doing mainly IO, or a CPU or GPU thread doing intensive computation.
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If you are, if you're bouncing back and forth between many different programming models, you're going to have more lines of code," Costa said.įor one, Nvidia is involved in a C++ committee that is laying down the piping that orchestrates parallel execution of code that is portable across hardware. "The complication is – it may be measured as simply as lines of code. The company is helping the community standardize best-in-class tools to write parallel code that is portable across hardware platforms, independent of brand, accelerator type or parallel programming framework. While parallel programming is widespread in HPC, Nvidia's goal is to standardize it in mainstream computing. But for those willing to invest, CUDA will provide that extra last-mile boost as it is tuned to work closely with the Nvidia's GPU. Programmers can use open-source parallel programming frameworks that include OpenCL on Nvidia's GPUs. Nvidia has two ways to earn money via concepts like the AI factory: through the utilization of GPU capacity or usage of domain-specific CUDA libraries. The concept is that customers can drop applications in Nvidia's mega datacenters, with the output being a customized AI model that meets specific sector or application requirements. The full-stack strategy is best illustrated by the concept of an "AI factory" introduced by CEO Jensen Huang at the recent GPU Technology Conference.
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