Tag: OpenCL


Thoughts on GPU thermal issues

I’ve been playing around with several devices in a context of running OpenCL code on them. They have one common thing which is excessive heat coming out of GPU and heatsink being unable to dissipate it. I start with MacBookPro3,1. It has NVIDIA 8600M GT, which is known to fail. I assume that it may be linked with overheating. Second example is design failure of Lenovo Thinkpad T420s which has built in NVIDIA NVS 4200M. This laptop has Optimus feature which in theory could detect if workload should be run on discrete or integrated GPU. Unfortunately enabling either Optimus or


Device performance in OpenCL DES

Among various computing devices I have there is one that stands out it is NVIDIA Quadro NVS 140M because it supports only FP32 (float) operations, but not FP64 (double). It is generally too old. In OpenCL we have both pow function which takes double and float parameters. The latter is called pown. I use first one to actually benchmark double precision computation. Model Year Core Unit Clk Perf 1k 10k 100k NVS 4200M 2011 48 1 1480 156/12 13 116 1163 Tesla K20xm 2012 2688 14 732 3935/1312 2 3 24 Intel i7 2640M 2011 2 4 2800 n/a 3


GPU passthru in Proxmox for OpenCL, ufff

Finally I got it working. I think so. This Proxmox installation is simple one, just single node for experiments which is half part. The other part is VM configuration. You may ask, what exactly for do I need GPU in VM? I may need because the hardware is capable of running several additional GPUs and I can use all of them at once in different configurations and even in different operation systems. Just like people do in cloud environments and this setup mimics such thing running server-like computer with datacenter-like GPUs on board. During this test I used NVIDIA GTX


Text processing in OpenCL

OpenCL is excellent in the field of numbers, but not that much into text processing. It lacks even basic functions available in regular C99. So the question is if it is worth trying to process some text in it. In my OpenCL base project (which can be found here) I’ve added “aiml” module. It loads over 31k lines of text with over 4 mln characters. The text itself is in the first buffer of uchar array. Second buffer holds pointers and lenghts of consecutive lines being work-items, so there are over 31k of such work-items. Third buffer is a result


DES cipher is 40 times faster in OpenCL

OpenCL implementation od DES cipher is way faster than regular single-threaded C program. 1 mln encryptions take less than a second on RTX 3050 Ti, but also as much as almost 40 seconds on Intel i5-10200h single-thread application. Lack of compact and extremely portable SSL/TLS library in pre C++11 project made me think about going for something easy to implement on my own concerning data encryption. I’ve selected DES, which stands for Data Encryption Standard because of my general understanding of such algorithm. It comes from 1975 and has certain limitations including short key length or known weak keys. But


Failed to load library libOpenCL.so.1

Today I came back to my OpenCL project (can be found here). I have had not tried it on my fresh Ubuntu 22 installation on my Dell g15 with RTX 3050 Ti. Altough I’ve got NVIDIA driver metapackage installed my program reported that there is some problem with shared library: You can check if the driver is selected in system About window. So there it is in my case, but nvidia-selector command reports that I have nvidia-driver-525 which is weird as drivers tab says I have installed driver 470. On the other hand, running nvidia-smi command says I have enable



Programowanie CPU i GPU “Mamy rok 2022. Minęła ponad dekada od pojawienia się OpenCL. Omówię standard, przykładowy sprzęt, a także zaprezentuję rzeczywiste przykłady, które można wykorzystywać obecnie zarówno na historycznym jak i najnowszym sprzęcie, co ma pokazać swego rodzaju uniwersalność tego rozwiązania. Przy okazji zobaczymy jak ewoluowała technologia na przestrzeni ostatnich lat i czy teoria odpowiada praktyce. Udostępnione przykłady pokażą, że dzięki stosowaniu standardu OpenCL możemy uzyskać nawet 50-krotne przyspieszenie działania algorytmu (na korzyść GPU) porównując czas pracy CPU i GPU. Zacznijmy jednak od podstaw, tak aby zrozumieć, dlaczego tak się dzieje…“

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