Comparing memory systems for chip multiprocessors
Proceedings of the 34th annual international symposium on Computer architecture
Gaussian KD-trees for fast high-dimensional filtering
ACM SIGGRAPH 2009 papers
GPU-based simulation of cellular neural networks for image processing
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Accuracy of GPU-based B-spline evaluation
CGIM '08 Proceedings of the Tenth IASTED International Conference on Computer Graphics and Imaging
Accelerating Scalar-Product Based Sequence Alignment using Graphics Processor Units
Journal of Signal Processing Systems
Structured parallel programming with deterministic patterns
HotPar'10 Proceedings of the 2nd USENIX conference on Hot topics in parallelism
Multifrontal computations on GPUs and their multi-core hosts
VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
Decoupling algorithms from schedules for easy optimization of image processing pipelines
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
Intel's Array Building Blocks: A retargetable, dynamic compiler and embedded language
CGO '11 Proceedings of the 9th Annual IEEE/ACM International Symposium on Code Generation and Optimization
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Many researchers have observed that general purpose computing with programmable graphics hardware (GPUs) has shown promise to solve many of the world's compute intensive problems, many orders of magnitude faster the conventional CPUs. The challenge has been working within the constraints of a graphics programming environment and limited language support to leverage this huge performance potential. GPU computing with CUDA is a new approach to computing where hundreds of on-chip processor cores simultaneously communicate and cooperate to solve complex computing problems, transforming the GPU into a massively parallel processor. The NVIDIA C-compiler for the GPU provides a complete development environment that gives developers the tools they need to solve new problems in computation-intensive applications such as product design, data analysis, technical computing, and game physics. In this talk, I will provide a description of how CUDA can solve compute intensive problems and highlight the challenges when compiling parallel programs for GPUs including the differences between graphics shaders vs. CUDA applications.