《Parallel and High Performance Programming with Python: Unlock parallel and concurrent programming in ...
我们先看一下 Python 的内置函数 map。 第一个参数 function 以参数序列中的每一个元素调用 function 函数,返回包含每次 function 函数返回值的迭代器(Python2 中 map 输出的是列表),我们用 list() 把迭代器转换为列表观察结果。 list(map(lambdax: x + 10, [1, 2, 3, 4, 5])) 输出: [11, 12, ...
我们先看一下 Python 的内置函数 map。 第一个参数 function 以参数序列中的每一个元素调用 function 函数,返回包含每次 function 函数返回值的迭代器(Python2 中 map 输出的是列表),我们用 list() 把迭代器转换为列表观察结果。 list(map(lambdax: x + 10, [1, 2, 3, 4, 5])) 输出: [11, 12, ...
Hands-On GPU Programming with Python and CUDA最新章节 查看全部 Leave a review - let other readers know what you think Other Books You May Enjoy Chapter 12 Where to Go from Here Chapter 11 Performance Optimization in CUDA Chapter 10 Working with Compiled GPU Code Chapter 9 Implementation of ...
accelerated computing and for using increasingly sophisticated CUDA code with a minimum of new syntax and jargon. With CUDA Python and Numba, you get the best of both worlds: rapid iterative development with Python combined with the speed of a compiled language targeting both CPUs and NVIDIA GPUs...
The rgpu package (see below for link) aims to speed up bioinformatics analysis by using the GPU. Thegcbdpackage implements a benchmarking framework for BLAS and GPUs. TheOpenCLpackage provides an interface from R to OpenCL permitting hardware- and vendor neutral interfaces to GPU programming. ...
To evaluate the benefits of using the GPU to solve second-order wave equations, we ran a benchmark study in which we measured the amount of time the algorithm took to execute 50 time steps for grid sizes of 64, 128, 512, 1024, and 2048 on an Intel® Xeon® Processor X5650 and ...
Covered GPU tech:Python Jupyter Notebooks, BlazingSQL, cuDF (DataFrames), cuML, Apache Arrow, Dask, cuXFilter, Datashader, Matplotlib... OPTIONAL LIVE LAB (1hr): Load and explore large file using GPUs Emphasis on cuDF and SQL Tech: Jupyter, cuDF, BlazingSQL, Dask, cuML, Datashader ...
@triton.jit decorator通过遍历提供Python函数的抽象语法树（AST），产生的Triton-IR使用通用SSA构建算法上...
Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll t