How Math Libraries Can Help Accelerate Your Applications on Blackwell GPUs

Overview

Discover how NVIDIA's GPU-accelerated math libraries in the CUDA Toolkit and HPC SDK can optimize performance for AI, ML, and scientific computing workflows on Blackwell GPUs.

Syllabus

• Introduction to Blackwell GPUs
Overview of Blackwell GPU architecture
Benefits of GPU acceleration in AI, ML, and scientific computing
• Introduction to CUDA Toolkit and HPC SDK
Overview of CUDA Toolkit
Overview of HPC SDK
• Understanding GPU-accelerated Math Libraries
Fundamentals of GPU-accelerated computing
Key math libraries in CUDA Toolkit
cuBLAS
cuFFT
cuRAND
cuSOLVER
cuSPARSE
High-performance math libraries in HPC SDK
• Utilizing cuBLAS for Linear Algebra
Overview of BLAS operations
Accelerating matrix multiplications
Applications in AI and ML
• Fast Fourier Transform with cuFFT
Introduction to FFT and its applications
Leveraging cuFFT for performance gains
• Random Number Generation with cuRAND
Importance of RNG in simulations
Utilizing cuRAND for efficient RNG
• Solving Systems of Equations with cuSOLVER
Overview of numerical methods for solving equations
Exploiting cuSOLVER for scientific computing
• Sparse Matrix Operations with cuSPARSE
Introduction to sparse matrices
Enhancing performance with cuSPARSE
• Practical Considerations and Optimization Techniques
Profiling GPU applications
Understanding memory hierarchies
Best practices for optimizing performance on Blackwell GPUs
• Case Studies and Real-world Applications
Examples of AI and ML applications
Scientific computing case studies
• Hands-on Workshop
Implementing math libraries in sample applications
Performance benchmarking and analysis
• Conclusion and Further Resources
Recap of course learnings
Additional resources for deeper exploration

Subjects

Programming