Does anyone know how to get a machine code program running on the Vice Vic-20 emulator? I keep getting the the emulator doing a reset every time i try it.
If i use the following code it should print an "A" on the screen but i don't see anything. Program is loaded at memory location $1001 for an unexpanded Vic-20
LDA #$01
STA $1E00
RTS
Thanks
Allan
Related
i'm doing verification to a RISCV cpu that supports Machine-Mode only, and i want to run my generated program on spike simulator.
i'm struggling to find any documentation about it.
how can i configure the first PC to my DUT first PC?
how can i configure other parameters like 'mvendorid' etc. ?
currently i'm working without pk and i'm getting "terminate called after throwing an instance of trap_load_access_fault".
when i'm working with pk, the program enters endless loop and the first PC doesn't look related to the ELF.
any suggestions?
Spike provides an option "--pc" which override the default entry point, run with '--pc DUT_FIRST_PC' should solve your problem.
When spike start simulation, its first pc is defined by DEFAULT_RSTVEC(0x1000), and spike set a "trampoline program" starts at DEFAULT_RSTVEC. After the "trampoline program" finished, spike jump to your program and go on, the jump pc can be override by '--pc' option.
First and foremost: I am completely unable to create a MCVE, as I can only reproduce this when running a full code, any attempt to measure or replicate the error in a simpler environment makes it disappear. TDLR I suspect its not a code problem, but a configuration problem.
I have a piece of code for some mathematics on kernels in CUDA. I have a windows machine Win10 x64, GTX 1050, CUDA 9.2 and a Ubuntu 17.04, 2xGTX 1080 Ti, CUDA 9.1.
My code runs good on the windows machine. It is long (~700ms per kernel call for big samples) so I needed to increase the TDR value in windows. The code also (for now) forces it to run in 1 GPU, the first one that is selected with cudaSetDevice(0).
When I copy the same input data and code to the linux machine (I am using git, it is the same code), I get either
an illegal memory access was encountered
or
unspecified launch failure
in my error checking after the GPU call.
If I change the kernel to instead do the math, to just write a number in the output, the kernel executes properly. Other CUDA code (different functions that I have) works fine too. All this leads me to think that there is a problem outside the code, not with the code itself, nor with the general configuration of the drivers/environment variables.
I read that the xorg.conf can have an effect on the timeout of the kernels. I generated a xorg.conf (I had none) and remove the devices from there, as suggested here. I am connecting to the server remotely, and have no monitor plugged in. This changes nothing in the behavior, my kernels still error.
My question is: what else should I look? What linux specific configuration should I have a look at to pinpoint the cause of the kernel halts?
The error ended up being indeed illegal memory access.
These were caused by the fact that sizeof(unsigned long) is machine specific, and my linux machine returns 8 while my windows machine returns 4. As this code is called from MATLAB, and MATLAB (like some other high level languages such as python) defines the sizes of variables in bits (such as uint32(1)) there was a mismatch in the linux machine when doing memcpys. Turns out that this happened in a variable that is a index, so the kernels were reading garbage (due to the bad memcpy), but then triying to access another array at that location, creating an illegal memory error.
Too specific? yeah.
Whatever program I ran on GPU, even if programs that ran successfully before, my GPU throws this error: CL_OUT_OF_RESOURCES for the clEnqueueReadBuffer function.
Then I remembered that I ran a deep learning framework last night which crashed and may ate up all the memory on GPU. I tried to restart the computer, but it doesn't work.
Is it possible that my GPU ran out of memory due to the DL framework's crash?
If so, how should I solve this problem?
CL_OUT_OF_RESOURCES is a generic error given by NVIDIA implementation at clEnqueueRead, it more or less means:
Something went out of bounds (resources) when trying to write to this
buffer
Most probably the kernel you launched before that writes to that buffer went out of bounds of the buffer.
I am booting up a linux kernel using a full system simulator, and I'd like to run my benchmark on the booted system. However, when it boots up, it shows me this message: "clocksource tsc unstable" and occasionally it hangs in the beggining. However, sometimes it lets me run my benchmark and then probably it hangs in the middle since the application never finishes and seems it's stuck there. Any idea how to fix this issue?
Thanks.
It suggests that, kernel didn't manage to calculate tsc (Time Stamp Counter) value properly i.e value is stale. This usually happens with VM. The way to avoid this is to - use predefined lpj (loops per jiffy) as kernel parameter (lpj=). Try it, hope issue will be fixed!
I'm running a small CUDA application: the QuickSort benchmark algorithm (see here). I have a dual system with a NVIDIA 660GTX (device 0) and 8600GTS (device 1).
Under Windows 8 and Visual Studio, the application compiles and runs flawlessly on device 0. Under Linux (Ubuntu 12.04 LTS), the app compiles with nvcc and gcc but suddenly stops in its tracks, returning a (unspecified launch failure).
I have two issues:
After this error, my GPU cannot perform some other operations, e.g., running the SDK example bandwidhtTest blocks when it performs the first data transfer, but running deviceQuery continues to perform well. How can I reset my GPU? I've already tried the cudaDeviceReset() method but it doesn't help
How can I find what is going wrong under linux? Has someone a clue or seen this before?
Thanks in advance for your help!
Using the nvidia-smi utility you can reset the GPU if it is compatible
To my knowledge and experience, (unspecified launch failure) usually referees to segmentation fault. Have you specified the right GPU to use? Try to use cuda-memcheck to see if there is any memory out of bound scenario.
From my experience XID 31 was always caused by accessing bad pointer (aka Memory access violation).
I'd first pursue this trail. Run your application with cuda memcheck. Like that cuda-memcheck you_app args to your app and see if it finds any wrong memory accesses.
Also try stepping though the code with cuda-gdb or Nsight Eclipse Edition.
I've found that using
cuda-memcheck -b ...
prevents the device from locking up.