gpgpu | 易学教程

From non coalesced access to coalesced memory access CUDA

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问题 I was wondering if there is any simple way to transform a non-coalesced memory access into a coalesced one. Let's take the example of this array: dW[[w0,w1,w2][w3,w4,w5][w6,w7][w8,w9]] Now, i know that if Thread 0 in block 0 access dW[0] and then Thread 1 in block 0 access dw[1] , that's a coalesced access in the global memory. The problem is that i have two operations. The first one is coalesced as described above. But the second one isn't because Thread 1 in block 0 needs to do an operation

Decrypting in pgpy fails with “ValueError: Expected: ASCII-armored PGP data”

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问题 I have an OpenPGP encrypted file and its private key in a text file and know its passphrase. I tried this below code: import pgpy emsg = pgpy.PGPMessage.from_file('PGPEcrypted.txt') key,_ = pgpy.PGPKey.from_file('PrivateKey.txt') with key.unlock('passcode!'): print (key.decrypt(emsg).message) But while trying to execute I am getting following error: Traceback (most recent call last): File "D:\Project\PGP\pgp_test.py", line 4, in <module> key,_ = pgpy.PGPKey.from_file('SyngentaPrivateKey.txt')

Decrypting in pgpy fails with “ValueError: Expected: ASCII-armored PGP data”

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How to reduce OpenCL enqueue time/any other ideas?

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问题 I have an algorithm and I've been trying to accelerate it using OpenCL on my nVidia. It has to process a large amount of data (let's say 100k to milions), where for each one datum: a matrix (on the device) has to be updated first (using the datum and two vectors); and only after the whole matrix has been updated, the two vectors (also on the device) are updated using the same datum. So, my host code looks something like this for (int i = 0; i < milions; i++) { clSetKernelArg(kernel

开发一套编程语言有多难？

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开发一套编程语言有多难？三丰 soft张三丰每个白天，我们都要扮演一个名副其实的成年人。兢兢业业工作，小心处理好和周围人的关系。到了深夜，你才属于自己。这一刻，你允许自己不完美，露出所有的伤口，你感慨，你哭泣。当深夜过去，你依旧是你，有软肋，更有盔甲。美好一天从“勇往直前”开始！开发一套编程语言，主要存在两个障碍，必要性其一：任何一种编程语言都有其存在的必要性，所以要搞明白为什么要弄一套新的编程语言，首先一定要有市场需求所在，编程语言如同一个产品，一定有强烈的市场需求，编程语言存在的价值在于生态链，只有具备完善的生态链才能存活的有意义，现在全球有600多种编程语言，主流的编程语言也就是几十种，而这些编程语言中，都有强大的社区依托，也就是强大的生态链支撑，生命力才会如此的强大。可扩展性本身的技术实现框架，有些编程语言在诞生之处就能感受到其强大的支配力，可扩展性等等特性，在设计之处越是想的明白，越是框架清晰，后期越容易维护，编程语言底层实现基本上靠的都是C语言，所以社会上很多对于内在不是很了解到人，说到C语言已经是过时的语言了，现在学习意义已经不大的论调，只不过在就业岗位绝对数量上，和java，python等高级编程语言无法比拟，但在一些关键岗位上离开了C语言还真不行，编写编程语言就属于这类的工作，一定需要强大的C语言做支撑。当然这个和就业无关。

CUDA compiler is unable to compile a simple test program

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问题 I am trying to get NVIDIA's CUDA setup and installed on my PC which has an NVIDIA GEFORCE RTX 2080 SUPER graphics card. After hours of trying different things and lots of research I have gotten CUDA to work using the Command Prompt, though trying to use CUDA in CLion will not work. Using nvcc main.cu -o build.exe From the command line generates the executable and I can run it on the GPU, however I have the following error when trying to use CLion: I believe this is the relevant part, however

CUDA/C - Using malloc in kernel functions gives strange results

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问题 I'm new to CUDA/C and new to stack overflow. This is my first question. I'm trying to allocate memory dynamically in a kernel function, but the results are unexpected. I read using malloc() in a kernel can lower performance a lot, but I need it anyway so I first tried with a simple int ** array just to test the possibility, then I'll actually need to allocate more complex structs. In my main I used cudaMalloc() to allocate the space for the array of int * , and then I used malloc() for every

CUDA/C - Using malloc in kernel functions gives strange results

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CUDA/C - Using malloc in kernel functions gives strange results

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Processing Shared Work Queue Using CUDA Atomic Operations and Grid Synchronization

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问题 I’m trying to write a kernel whose threads iteratively process items in a work queue. My understanding is that I should be able to do this by using atomic operations to manipulate the work queue (i.e., grab work items from the queue and insert new work items into the queue), and using grid synchronization via cooperative groups to ensure all threads are at the same iteration (I ensure the number of thread blocks doesn’t exceed the device capacity for the kernel). However, sometimes I observe