OpenCV for DSP/GPU, MSDN equivalent for CCS, and more
Porting OpenCV to DSPs could be a real business opportunity, but it is far from trivial, writes Shehrzad Qureshi. He highlights major obstacles: the engineering scale, mixed open-source licenses, and hard-to-parallelize primitives like connected components. He also criticizes Code Composer Studio's help system compared with MSDN, notes an ATI Stream talk, and announces a CUDA walkthrough on FFT-based image filtering.
Random GPGPU Musings
Shehrzad Qureshi argues that general-purpose GPU computing is poised to reshape engineering workloads, and contrasts Nvidia's CUDA ecosystem with ATI's Stream and OpenCL. He points out that GPU architectures and programming models are similar across vendors, but Nvidia's head start in sample code and developer community gives CUDA a practical advantage. Read for a concise industry perspective on choosing a GPGPU platform.
GPGPU DSP
Shehrzad Qureshi kicks off his DSP blog by championing GPGPU, focusing on Nvidia's CUDA and real-product experience. He argues that with CPU clock speeds stalled, large-scale parallelism on GPUs is the practical path forward for many signal-processing tasks. The post traces GPGPU history from shader 'hacks' to modern APIs and previews future posts comparing CUDA vs OpenCL, Intel's Larrabee, and Nvidia Fermi.
OpenCV for DSP/GPU, MSDN equivalent for CCS, and more
Porting OpenCV to DSPs could be a real business opportunity, but it is far from trivial, writes Shehrzad Qureshi. He highlights major obstacles: the engineering scale, mixed open-source licenses, and hard-to-parallelize primitives like connected components. He also criticizes Code Composer Studio's help system compared with MSDN, notes an ATI Stream talk, and announces a CUDA walkthrough on FFT-based image filtering.
GPGPU DSP
Shehrzad Qureshi kicks off his DSP blog by championing GPGPU, focusing on Nvidia's CUDA and real-product experience. He argues that with CPU clock speeds stalled, large-scale parallelism on GPUs is the practical path forward for many signal-processing tasks. The post traces GPGPU history from shader 'hacks' to modern APIs and previews future posts comparing CUDA vs OpenCL, Intel's Larrabee, and Nvidia Fermi.
Random GPGPU Musings
Shehrzad Qureshi argues that general-purpose GPU computing is poised to reshape engineering workloads, and contrasts Nvidia's CUDA ecosystem with ATI's Stream and OpenCL. He points out that GPU architectures and programming models are similar across vendors, but Nvidia's head start in sample code and developer community gives CUDA a practical advantage. Read for a concise industry perspective on choosing a GPGPU platform.
OpenCV for DSP/GPU, MSDN equivalent for CCS, and more
Porting OpenCV to DSPs could be a real business opportunity, but it is far from trivial, writes Shehrzad Qureshi. He highlights major obstacles: the engineering scale, mixed open-source licenses, and hard-to-parallelize primitives like connected components. He also criticizes Code Composer Studio's help system compared with MSDN, notes an ATI Stream talk, and announces a CUDA walkthrough on FFT-based image filtering.
GPGPU DSP
Shehrzad Qureshi kicks off his DSP blog by championing GPGPU, focusing on Nvidia's CUDA and real-product experience. He argues that with CPU clock speeds stalled, large-scale parallelism on GPUs is the practical path forward for many signal-processing tasks. The post traces GPGPU history from shader 'hacks' to modern APIs and previews future posts comparing CUDA vs OpenCL, Intel's Larrabee, and Nvidia Fermi.
Random GPGPU Musings
Shehrzad Qureshi argues that general-purpose GPU computing is poised to reshape engineering workloads, and contrasts Nvidia's CUDA ecosystem with ATI's Stream and OpenCL. He points out that GPU architectures and programming models are similar across vendors, but Nvidia's head start in sample code and developer community gives CUDA a practical advantage. Read for a concise industry perspective on choosing a GPGPU platform.
