I want to collect power traces of CPU on the real device TI CC26XX running on Contiki-NG. I checked Contiki-NG source code and it supports clock.c and driver for CC26XX at
https://github.com/contiki-ng/contiki-ng/blob/develop/arch/cpu/cc26x0-cc13x0/clock.c
https://github.com/contiki-ng/cc26xxware/tree/master/driverlib
Someone can gives some ideas or code how to collect power consumption based on its driver and clock?
Many thanks!
Related
When I run code on my colab notebook after having selected the GPU, I get a message saying "You are connected to a GPU runtime, but not utilizing the GPU". Now I understand similar questions have been asked before, but I still don't understand why. I am running PCA on a dataset over hundreds of iterations, for multiple trials. Without a GPU it takes about as long as it does on my laptop, which can be >12 hours, resulting in a time out on colab. Is colab's GPU restricted to machine learning libraries like tensorflow only? Is there a way around this so I can take advantage of the GPU to speed up my analysis?
Colab is not restricted to Tensorflow only.
Colab offers three kinds of runtimes: a standard runtime (with a CPU), a GPU runtime (which includes a GPU) and a TPU runtime (which includes a TPU).
"You are connected to a GPU runtime, but not utilizing the GPU" indicates that the user is conneted to a GPU runtime, but not utilizing the GPU, and so a less costly CPU runtime would be more suitable.
Therefore, you have to use a package that utilizes the GPU, such as Tensorflow or Jax. GPU runtimes also have a CPU, and unless you are specifically using packages that exercise the GPU, it will sit idle.
I have a simple anisotropic filter c/c++ code that will process an .pgm image which is an text file with greyscale information for each pixel, and after done processing, it will generate an output image with the filter applied.
This program takes up to some seconds in order for it to do about 10 iterations on a x86 CPU running windows.
Me and an academic finishing his master degree on applied computing, we need to run the code under FPGA (Altera DE2-115) to see if there is considerable results of performance gain when running the code directly on the processor (NIOS 2).
We have successfully booted up the S.O uClinux under the FPGA, but there are some errors with device hardware, and by that we can't access SD-Card not even Ethernet, so we can't get the code and image into the FPGA in order to test its performance.
So I am here asking to an alternative way to test our code performance directly into an CPU with a file system so the code can read the image and generate another one.
The alternative can be either with an product that has low cost and easy to use (I was thinking raspberry PI), or either if I could upload the code somewhere that runs automatically for me and give me the reports.
Thanks in advance.
what you're trying to do is benchmarking some software on a multi GHz x86 Processor vs. a soft-core processor running 50MHz? (as much as I can tell from Altera docs)
I can guarantee that it will be even slower on the FPGA! Since it is also running an OS (even embedded Linux) it also has threading overhead and what not. This can not be considered running it "directly" on CPU (whatever you mean by this)
If you really want to leverage the performance of an FPGA you should "convert" your C-Code into a HDL and run it directly in hardware. Accessing the data should be possible. I don't know how it's done with an Altera board but Xilinx has some libraries accessing data from a SD card with FAT.
You can use on board SRAM or DDR2 RAM to run OS and your application.
Hardware design in your FPGA must have memory controller in it. In SOPC or Qsys select external memory as reset vector and compile design.
Then open NioSII build tools for Eclipse.
In Eclipse create new project by selecting NiosII Application and BSP project.
Once the project is created, go to BSP properties and type offset of external memory in the linker tab and generate BSP.
Compile project and Run as Nios II hardware.
This will run you application on through external memory.
You wont be able to see the image but 2-D array representing image in memory can be
printed on console.
I am really puzzled here. I want to create an application that does different events upon different temperatures of my graphics card which is an AMD one.
The reason i want to make such an applications is because, for a GPU i haven't found one, and the second is to ensure i never fry my card by reaching enormous temps.
However i have no idea how people(not connected to amd/intel/nvidia) write applications to monitor temperatures of any kind.
So how does it happen? Some APIs i don't know or something?
After a little bit of googling, i found this:
I think this is really vendor specific, it will probably involve interfacing directly with the motherboard or video driver and knowing which IOCTL represents the code for requesting the temperature. I reverse engineered a motherboard driver once for this purpose. It's not as hard as it sounds, download a manufacturer motherboard/BIOS utility and try to hook the function that gets called when that app needs to display the temperature to the user. Then watch for a call to DeviceIoControl() in Windows, or ioctl() in linux and see what the inputs / outputs are.
This may be your best bet. I found this information here:
http://www.gamedev.net/topic/557599-get-gpucpu-temperature/
Edit:
Also found this:
http://msdn.microsoft.com/en-us/library/aa389762%28v=VS.85%29.aspx
http://msdn.microsoft.com/en-us/library/aa394493%28VS.85%29.aspx
Hope it helps.
You could use one of the existing GPU temperature monitoring programs, such as GPU-Z, configure it for continuous monitoring, and read the log entries.
RivaTuner is another GPU monitoring program, which has a shared memory interface allowing other programs to get the data in real-time, but is nVidia focused. As long as your action isn't "reduce the GPU clock speed" it'll probably work well enough with ATI cards.
I am trying to optimize my OpenCL kernels and all I have right now is NVidia Visual Profiler, which seems rather constrained. I would like to see line-by-line profile of kernels to better understand issues with coalescing, etc. Is there a way to get more thorough profiling data than the one, provided by Visual Profiler?
I think that AMD CodeXL is what you are looking for. It's a free set of tools that contains an OpenCL debugger and a GPU profiler.
The OpenCL debugger allows you to do line-by-line debugging of your OpenCL kernels and host code, view all variables across different workgroups, view special events and errors that occur, etc..
The GPU profiler has a nice feature that generates a timeline displaying how long your program spends on tasks like data transfer and kernel execution.
For more info and download links, check out http://developer.amd.com/tools-and-sdks/heterogeneous-computing/codexl/
No, there is no such tool but you can profile your code changes. Try measuring the speed of your code, change something and then measure it once again. clEnqueueNDRangeKernel has an Event argument which can be used with clGetEventProfilingInfo afterwards, the timer is very sharp, the accuracy is measured in order of microseconds. This is the only way to measure performance of a separate code part...
I haven't test it but I just found this program http://www.gremedy.com/gDEBuggerCL.php.
The description is: " This new product brings gDEBugger's advanced Debugging, Profiling and Memory Analysis abilities to the OpenCL developer's world..."
LTPV is an open-source, OpenCL profiler that may fit your requirements. It is, for now, only working under Linux.
(disclosure: I am the developer of this tool)
Is is possible to measure CPU per thread on a windows mobile (or CE 5) device programmatically (c++)? If not, is their a utility that will monitor the CPU usage of a process?
CPU usage cannot be directly measured because, unlike an x86, the ARM processor doesn't have a register for it. You can calculate it using the Toolhelp APIs to get a list of processes and their child threads and then use GetThreadTimes to figure out how much time each thread is using.
Keep in mind that doing this calculation directly affects how much the CPU is in use.
Someone wrote a tool that looks a lot like Task Manager on the PC:
http://www.vttoth.com/LPK/taskmanager.html
As ctacke says, it does seem to use a lot of the CPU. It reports uses ~15%-30% of our CPU on our 800MHz ARM device.