The “Intel_GPU_top” tool for Linux uses a number of acronyms (i.e. GAM, CS, SDE, SVG, VS, VF, GAFS, CL, RS,TSG, VFE,SF, IDG, GS,SOL GAFM and DS). Is there a glossary of terms available that anyone could point me to?
The problem is that :
I want to transcode mpegts instead of a local file(mp4, mpeg2, h264…), the ts is generated by ffmpeg from a local mp4 file, the output of ffmpeg is :
'udp://10.15.10.78:8090, the format is mpegts. I use the sample_full_transcode_drm to transcode :
$ ./sample_full_transcode_drm -i udp://localhost:8090 -o out.mp4 -v:b 2000 -hw
But failed, the error is:
cannot open file: udp://localhost:8090
I have checked that a mp4 file(without audio) or a mpeg2 can be transcoded successfully.
I am writing this post to ask for help about installing the intel haxm, I previously used win8, and I could install the haxm without any problems, but a few
days ago, I change the os to win7 64bit, while I cannot install the haxm after I tried many ways to fix it.
I refered to the the page : https://software.intel.com/en-us/blogs/2014/03/14/troubleshooting-intel-...
and tried the ways that mentioned in the page, but nothing helped. I went to bios and enabled the VT.
below are the info captured about my pc.
hoping to get any helpful info from you guys, many thanks.
I want to apply the HAXM on the QEMU for my x86 virtual machine.
The QEMU for the Tizen and the Android is the open source.
So, I am analyzing and referencing them.
But, the Android's QEMU is very old version.
So, my custom QEMU is based on the Tizen's QEMU.
Recently, I checked the issue related with HAXM version.
The Tizen QEMU use the HAXM 1.0.5, and the Android QEMU use the HAXM 1.1.1.
The Tizen QEMU cannot run and close the emulator on the HAXM 1.1.1.
It can run and close on the HAXM 1.0.5.
While GraphX provides nice abstractions and dataflow optimizations for parallel graph processing on top of Apache Spark*, there are still many challenges in applying it to an Internet-scale, production setting, e.g. graph algorithms and underlying frameworks optimized for billions of graph edges and 1000s of iterations. This presentation, will show our efforts in building real-world, large-scale graph analysis applications using GraphX for some of the largest organizations/websites in the world, including both algorithm level and framework level optimizations, e.g.