NVIDIA CUDA SDK x64 - Performance Strategies

The CUDA Developer SDK provides examples with source code, utilities, and white papers to help you get started writing software with CUDA. The SDK includes dozens of code samples covering a wide range of applications including:

Simple techniques such as C++ code integration and efficient loading of custom datatypes
How-To examples covering CUDA BLAS and FFT libraries, texture fetching in CUDA, and CUDA interoperation with the OpenGL and Direct3D graphics APIS
Linear algebra primitives such as matrix transpose and matrix-matrix multiplication
Data-parallel algorithms such as parallel prefix sum of large arrays
Performance: profiling using timers and bandwidth tests
Advanced application examples such as image convolution, Black-Scholes options pricing and binomial options pricing

Refer to the following READMEs for more information ( Linux , Windows )

This code is released free of charge for use in derivative works, whether academic, commercial, or personal. (Full License)

The NVIDIA CUDA Toolkit is required to run and compile code samples. Please obtain the CUDA Toolkit here

Quick Links:

Data-Parallel Algorithms	Computational Finance
Performance Strategies	Linear Algebra
Physically-Based Simulation	CUDA Basic Topics
Graphics Interop	Image/Video Processing and Data Compression
CUDA Advanced Topics


Monte-Carlo Option Pricing with multi-GPU support This sample evaluates fair call price for a given set of European options using Monte-Carlo approach, taking advantage of all CUDA-capable GPUs installed in the system.		or later Download - Windows Download - Linux


Matrix Transpose Efficient matrix transpose.		or later Download - Windows Download - Linux


Clock This example shows how to use the clock function to measure the performance of kernel accurately.		or later Download - Windows Download - Linux


Aligned Types A simple test, showing huge access speed gap between aligned and misaligned structures.		or later Download - Windows Download - Linux


Scan This example demonstrates an efficient CUDA implementation of parallel prefix sum, also known as "scan". Given an array of numbers, scan computes a new array in which each element is the sum of all the elements before it in the input array.		or later Whitepaper Download - Windows Download - Linux


Bandwidth Test This is a simple test program to measure the memcopy bandwidth of the GPU. It currently is capable of measuring device to device copy bandwidth, host to device copy bandwidth for pageable and page-locked memory, and device to host copy bandwidth for pageable and page-locked memory.		or later Download - Windows Download - Linux


Scan of Large Arrays This example demonstrates an efficient CUDA implementation of parallel prefix sum (also known as "scan") for arbitrary-sized arrays. Given an array of numbers, scan computes a new array in which each element is the sum of all the elements before it in the input array.		or later Whitepaper Download - Windows Download - Linux


Parallel Reduction A parallel sum reduction that computes the sum of large arrays of values. This sample demonstrates several important optimization stratezies for parallel algorithms like reduction.		or later Whitepaper Download - Windows Download - Linux


asyncAPI This sample uses CUDA streams and events to overlap execution on CPU and GPU.		or later Download - Windows Download - Linux


simpleStreams This sample uses CUDA streams to overlap kernel executions with memcopies between the device and the host.		or later Download - Windows Download - Linux