Explore featured sessions for Developers.

Get training, insights, and access to experts on the latest in GPU programming for developers. Sessions will cover topics such as performance optimization and profiling, debuggers and code analysis, programming languages and compilers, and libraries and runtimes.

Featured Speakers

Zoe Ryan

GPU Acceleration in Python using CuPy and Numba

Zoe Ryan 
Solutions Architect, NVIDIA

Jonas Latt
Venkatesh Ramanathan

Large-Scale Graph Neural Networks Optimization for Financial Application

Venkatesh Ramanathan
Director of Data Science, PayPal

John Owens

Dynamic Data Structures on the GPU

John Owens
Professor, UC Davis

Sessions By Topics

Accelerated Computing & Dev Tools

  • Accelerated Computing with Standard C++, Python, and Fortran

    • Jeff Larkin, Principal HPC Architect, NVIDIA

    Programmers can now write parallel-first code, adding parallelism as a baseline rather than an afterthought. This session highlights recent advances in native language parallelism in C++, Python, and Fortran and shows how programmers can begin to support GPU acceleration as a native feature in their applications with no specialized code.

  • Multi-GPU Programming Models

    • Jiri Kraus, Principal DevTech Compute, NVIDIA

    Learn how to scale your application to multiple GPUs and multiple nodes. You’ll see how to use the different available multi-GPU programming models and describe their individual advantages. All programming models—including CUDA-aware MPI, NVSHMEM, and NCCL—will be introduced using the same example, applying a domain decomposition strategy.

  • Accelerate Computing with CUDA Python

    • Mike McCarty, Senior Product Manager, NVIDIA

    NVIDIA is unifying the CUDA Python ecosystem with a single standard low-level API that provides access to the CUDA host APIs directly from Python. Recently released on GitHub, CUDA Python provides a foundation for the ecosystem to build libraries that target NVIDIA GPUs. This session dives into the challenges that motivated the project and gives an overview of the library features, illustrated by an example workflow.

See GPU programming session highlights from the previous GTC. Get ready for what’s to come.

Find the complete GTC On-Demand playlist here.

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