The CUDA 4.1 release candidate2 (RC2) 

Jump to downloads for : [Windows][ Linux ] [ MacOS ] 

This release includes a new LLVM-based CUDA compiler, 1000+ new image processing functions, and a redesigned Visual Profiler with automated performance analysis and integrated expert guidance.

We’re looking forward to hearing about your experience with this release (good and bad) through the Registered Developer feedback form. Give feedback via the CUDA Registered Developer Program .

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

Watch the CUDA Toolkit 4.1 Feature and Overview Webinar  for an overview of some of the exciting new features of this release.
 
Find all the latest versions of other Libraries and Tools on our Tools & EcoSystem Page

Get yourself fully trained- check out the latest CUDA Webinars
Become a CUDA Registered Developer, report bugs, engage with NVIDIA engineering
Jump to: [Windows][ Linux ] [ MacOS ]