CUDA
NVIDIA CUDA 13.1 Drops CUB Boilerplate with New Single-Call API
NVIDIA simplifies GPU development with CUB single-call API in CUDA 13.1, eliminating repetitive two-phase memory allocation code without performance loss.
NVIDIA cuTile Python Guide Shows 90% cuBLAS Performance for Matrix Ops
NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code.
NVIDIA Enhances cuML Accessibility by Reducing CUDA Binary Size for PyPI Distribution
NVIDIA introduces pip-installable cuML wheels on PyPI, simplifying installation and broadening accessibility by reducing CUDA binary sizes.
NVIDIA Enhances Memory Safety with Compile-Time Instrumentation for Compute Sanitizer
NVIDIA's latest update to Compute Sanitizer introduces compile-time instrumentation to improve memory safety in CUDA C++ applications, reducing false negatives and enhancing bug detection.
NVIDIA's ComputeEval 2025.2 Challenges LLMs with Advanced CUDA Tasks
NVIDIA expands ComputeEval with 232 new CUDA challenges, testing LLMs' capabilities in complex programming tasks. Discover the impact on AI-assisted coding.
Enhancing GPU Efficiency: Understanding Global Memory Access in CUDA
Explore how efficient global memory access in CUDA can unlock GPU performance. Learn about coalesced memory patterns, profiling techniques, and best practices for optimizing CUDA kernels.
NVIDIA Enhances Vision AI with CUDA-Accelerated VC-6
NVIDIA introduces CUDA-accelerated VC-6 to optimize vision AI pipelines, leveraging GPU parallelism for high-performance data processing, reducing I/O bottlenecks, and enhancing AI application efficiency.
Enhancing CUDA Kernel Performance with Shared Memory Register Spilling
Discover how CUDA 13.0 optimizes kernel performance by using shared memory for register spilling, reducing latency and improving efficiency in GPU computations.
NVIDIA Introduces Wheel Variants to Simplify CUDA-Accelerated Python Package Deployment
NVIDIA launches Wheel Variants to streamline CUDA-accelerated Python package installation, addressing compatibility challenges and optimizing user experience across diverse hardware setups.
Enhancing CUDA Performance: The Role of Vectorized Memory Access
Explore how vectorized memory access in CUDA C/C++ can significantly improve bandwidth utilization and reduce instruction count, according to NVIDIA's latest insights.
NVIDIA's CUTLASS 4.0: Advancing GPU Performance with New Python Interface
NVIDIA unveils CUTLASS 4.0, introducing a Python interface to enhance GPU performance for deep learning and high-performance computing, utilizing CUDA Tensors and Spatial Microkernels.
NVIDIA Expands Python Capabilities with CUDA Kernel Fusion Tools
NVIDIA introduces cuda.cccl, bridging the gap for Python developers by providing essential building blocks for CUDA kernel fusion, enhancing performance across GPU architectures.
Exploring Handwritten PTX Code for GPU Optimization in CUDA
Delve into the potential of handwritten PTX code for enhancing GPU performance in CUDA applications, as outlined by NVIDIA experts.
Enhancing CUDA Development: Compiler Explorer Unveiled
Compiler Explorer is revolutionizing CUDA development by offering a seamless web-based platform for writing, compiling, and running GPU kernels, fostering collaboration and innovation.
NVIDIA's cuEmbed Boosts GPU Performance for Embedding Lookups
NVIDIA unveils cuEmbed, a CUDA library that significantly enhances embedding lookups on GPUs, promising improved performance for recommendation systems and other applications.
