The folks here at Intel see me as a bit of an Ubuntu fanboy. For the most part, I spend a lot of time in the lab working on the ONP Server using CentOS. For me, it is less about being an Ubuntu fan and more about using a platform that is easier choice for Openstack developers. I recently had an opportunity to peel off an extra ONP Server and install the new Ubuntu 15.04 to have a look.
Many developers worldwide are taking advantage of the Intel® RealSense™ technologies to create really amazing applications with a whole new dimension for user interaction.
Intel® provides an SDK that exposes all the features of the R200 and F200 cameras. The SDK is very powerful but it requires some time to find your way into it. To help people to get up and running with just a few lines of code I started SharpSenses.
I have two questions about WRITE/READ operations on shared arrays.
1) In my program I write a different element of a given shared array at every iteration of an OpenMP-parallelized DO LOOP. The results that I get should be right but I'm just wondering whether this is fine or I should enclose the READ/WRITE section in a CRITICAL block. Then, I also READ elements from a shared array without modifying them and it seems to work. Are these procedures correct?
Here I am back at the AWS Loft giving a workshop on the topic of connecting Intel Edison to AWS. This time we got a little crazy and started incorporating some Johnny-Five and Node.js-based tracks for interested developers!
# ProductName: Mac OS X
# ProductVersion: 10.10.3
# BuildVersion: 14D136
curl -O https://www.openmprtl.org/sites/default/files/libomp_20150401_oss.tgz
gunzip -c libomp_20150401_oss.tgz | tar xopf -
in line 124..126 of libomp_oss/src/makefile.mk:
ifeq "$(os)" "mac"
mac_os_new := $(shell /bin/sh -c 'if ; then echo "1"; else echo "0"; fi')
I'm new in using OpenMP. I would like to ask about speedup ratio.
I running C source code with OpenMP added with Intel core i5-2410M.
Based on my understanding, speedup = execution time of code using one thread/execution time of code using N threads
The execution time recorded is time_diff in the attached code.
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