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Nvidia CUDA Toolkit for Mac OS 4.1.21 RC2

Nvidia CUDA Toolkit for Mac OS 4.1.21 RC2

Nvidia CUDA Toolkit for Mac OS Publisher's Description

UDA Toolkit :
- C/C++ compiler
- Visual Profiler
- GPU-accelerated BLAS library
- GPU-accelerated FFT library
- GPU-accelerated Sparse Matrix library
- GPU-accelerated RNG library
- Additional tools and documentation

Release Highlights

Try The New Compiler!
- New LLVM-based compiler delivers up to 10% faster performance for many applications

New & Improved “Drop-In” Acceleration With GPU-Accelerated Libraries
- Over 1000 new image processing functions in the NPP library
- New cuSPARSE tri-diagonal solver up to 10x faster than MKL on a 6 core CPU
- New support in cuRAND for MRG32k3a and Mersenne Twister (MTGP11213) RNG algorithms
- Bessel functions now supported in the CUDA standard Math library
- Up to 2x faster sparse matrix vector multiply using ELL hybrid format
- Learn more about all the great GPU-Accelerated Libraries

Enhanced & Redesigned Developer Tools
- Redesigned Visual Profiler with automated performance analysis and expert guidance
- CUDA_GDB support for multi-context debugging and assert() in device code
- CUDA-MEMCHECK now detects out of bounds access for memory allocated in device code
- Parallel Nsight 2.1 CUDA warp watch visualizes variables and expressions across an entire CUDA warp
- Parallel Nsight 2.1 CUDA profiler now analyzes kernel memory activities, execution stalls and instruction throughput
- Learn more about debugging and performance analysis tools for GPU developers on our CUDA Tools and Ecosystem Summary Page

Advanced Programming Features
- Access to 3D surfaces and cube maps from device code
- Enhanced no-copy pinning of system memory, cudaHostRegister() alignment and size restrictions removed
- Peer-to-peer communication between processes
- Support for resetting a GPU without rebooting the system in nvidia-smi

New & Improved SDK Code Samples
- SimpleP2P sample now supports peer-to-peer communication with any Fermi GPU
- New grabcutNPP sample demonstrates interactive foreground extraction using iterated graph cuts
- New samples showing how to implement the Horn-Schunck Method for optical flow, perform volume filtering, and read cube map texture

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