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Certified conformance by the Khronos OpenCL Working Group on 12 June 2009
Includes support for OpenCL Images and atomics, which enable significant acceleration across many image processing
disciplines. For example Medical Imaging, Video Transcoding applications, Machine Vision, Facial Detection and Recognition
and more via the following extensions:
cl_khr_byte_addressable_store
cl_khr_global_int32_base_atomics
cl_khr_global_int32_extended_atomics
cl_khr_local_int32_base_atomics
cl_khr_local_int32_extended_atomics
OpenCL Visual Profiler leverages performance instrumentation in NVIDIA's OpenCL drivers and hardware performance signals
designed into NVIDIA GPUs. This powerful analysis tool provides developers with insight into performance bottlenecks and
opportunities via these key features:
Profiling of actual hardware signals, kernel efficiency, and instruction issue rate
Timing of memory copies between system memory and GPU dedicated memory
Customizable graphs to help developers focus in on problem areas
Basic auto-analysis to reveal warp serialization problems
Easy import/export to CSV for custom analysis
Support for multi-GPU performance scaling has been added to most of the OpenCL code samples, and several new code samples
have been added as well, including:
oclMedianFilter
oclFDTD3d
oclRadixSort
oclMersenneTwister
oclSemirandomGenerator
OpenCL Best Practices Guide, designed to help developers using OpenCL on the CUDA architecture implement high
performance parallel algorithms and understand best practices for GPU Computing.
Chapters on the following topics and more are included in the guide:
Heterogeneous Computing with OpenCL
Performance Metrics
Memory Optimizations
NDRange Optimizations
Instruction Optimizations
Control Flow
Performance Optimization Strategies
The drivers and SDK code samples in this release are compatible with
with the publicly available CUDA Toolkit 2.3, available at
www.nvidia.com/cuda.
