Just make sure you have it well seated as there is no clip to tell you...Upside down 6 pin.. 2 yellow, 2 black lines up. Very nice Thank you.
Just make sure you have it well seated as there is no clip to tell you...Upside down 6 pin.. 2 yellow, 2 black lines up. Very nice Thank you.
How do you have the board grounded? Out of chassis is not ideal....IPMI isn't saying anything.
I plugged into the IPMI into the router and it is not negotiating also tried Eth1 with no result.
Used IPMI View 2.0/1.5 nothing on 192. network.
Checked router nothing there either.
Does it default to a 10. network... IPMI is a new tool for me.
RTFM'ing ftp://ftp.supermicro.com/utility/IPMIView/IPMIView20.pdf
I'm not surprised! I don't have that much experience with the Phi cards. I was under the impression that a lot of those libraries ran on Cuda - I know matlab and Theano do. I read that Phi can help with Intel MKL on the fly. Have you found they make a huge difference or do you have software that is specifically using them?R, numpy, scipy, matlab, Intel compiler, GCC, Fortran, opencl/acc, hopefully gnubackgammon (gnubg)
It is a compute node for math, kaggle contests, data mining, smp programming, distributed programming.
I am interested in studying math, data mining, parallel programming, and machine learning so I am building a fast bottom 500 supercomputer. This will hopefully be about a 3 node version of the tianhe-2 sometime this year. The tianhe-2 has 10,000+ nodes.
My little network should provide an environment with n nodes that exercises all the latency issues that occur in a supercomputing environment using tools that will allow me to take my code and scale them out to a large network.
Many of the NN libraries have been ported to CUDA. A Phi is about 1 Teraflop of fp64. For a lot of the NN libraries the video card shaders can do the linear algebra using fp32 faster than the general processors on a PHI. They are essentially a pared down pentium V cores plus some fast math and fp64/32 instructions with 8gb of shared ddr5 running busybox. So the outcome is essentially that of a 50 machine pentium V network running whatever code you choose optimized for the compute instruction set.I'm not surprised! I don't have that much experience with the Phi cards. I was under the impression that a lot of those libraries ran on Cuda - I know matlab and Theano do. I read that Phi can help with Intel MKL on the fly. Have you found they make a huge difference or do you have software that is specifically using them?
I have interests in Genetic Programming and NNs. Hoping to play around with some of the "newer" reinforcement algorithms once I get my E5-2675s up and running.
Good stuff!
The pcie connectors will fit into the 8pin doing this? Looking at the plug shapes, it doesn't look like it should work, but maybe it depends on what the motherboard's eps connector looks like.Don't use ****ing adapters... unless you want to start a fire.
Either A, don't plug a 4pin in...
Or b, plug twin 6 pin pcie upside down into 1 of the 8pins... you will have 1 pins hanging off each end... meh.
One of my SM AMD 4p rigs need 3 8pins... that worked wonderfully... they guys who used adapters started fires, I also overclocked that rig to where it drew 1450w from the wall.