Kernel Ost To Pst Converter 13.05.01 Cracked
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How can I improve the following code, that is, make it more robust with respect to type safety and endianness using the functions and macros in the Linux kernel's API? For instance, in the following example src_data is an array of two 16-bit signed integers (typically stored in little endian order) and is to be sent out via UART in big endian byte order.
The advantage of bit shifts compared to any other version is that they work on an abstraction level above the hardware and endianess, letting the specific compiler generate the instructions for the underlying memory access. Code such as u16 >> 8 always means "give me the least significant byte" regardless of where that byte is stored in memory.
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The following macros return the value after it has been converted. Note: the linux/kernel.h header file is the header file that should be included in the source files where these macros are used, but it is not the header file where the macros are actually defined.
Linux will not convert to C++ as long as Linus is running the show. Zig is too unstable at the moment to be the main work-horse but it can be a good language for some niche device drivers. It can do anything C does and more.
I think it could be interesting as a showcase/experiment, but personally I think a better way is to work on the language and ecosystem, until it becomes the best choice and people just start using it by their own choice.
My point is that once Zig becomes mature and more people become aware of its existence and features, it will most certainly make its way into the Linux kernel. At the very least, as a toolchain, but more realistically, as an easy path to maintain/extend/rewrite modules of the kernel.
I'm using Ubuntu 10.10 and the installed kernel for driver programming. In rubini's book it is mentioned to create a .o file from a .c file, but I'm not getting it.it uses obj-m := eg_drv1.o, but when I try and build the .c file I get this:
UPDATE: I have just tried and found that copy and paste is not working. I have to manually enter the contents of Makefile to work it out. Anyway it works fine. Just try and let us know the result.
This is a short document describing the preferred coding style for the linux kernel.Coding style is very personal, and I won'tforcemy views on anybody, but this is whatgoes for anything that I have to be able to maintain, and I'd prefer it for most other thingstoo. Please at least consider the points made here:
After upgrading to new kernel 5.4.0-88-generic, ESP266 development boards using chip CH340G (usb-serial converter) stop communicating with Espressif flashing tool "esptool" on my ubuntu 20.04 Dell laptop. The tool is written in python and is designed to be run on command line.I solved temporarily the issue by changing grub to boot on 5.4.0-86, but it would be a problem if this failure continues on next kernel upgrades.I wrote a simple code to test usb/serial direct communication using a simple serial terminal (moserial), and in this case the CH340G chip seems to work correctly in both directions, so my guess is there is a problem with python accessing the chip driver in the new kernel. I dont know where to report kernel issues so I decided to mention the problem in this forum.If this is not the right place I'd appreciate if some of you could tell me what would be the best way to report this to the appropiate channel.
Thanks!As I mentioned I downgraded temporarily the kernel to 5.4.0-86 and will wait to see if the issue is solved in the next version, but taking partially your very nice suggestion I copied the working CH341 driver from /lib/modules/5.4.0-86-generic/kernel/drivers/usb/serial to home, just in case the failure continued in next kernel.
BTW, I assume those magic numbers in your code are actually fractions of some sort, e.g. 1.16438 = 298/256? If so, it would be clearer to write them accordingly so someone who reads the code can more easily see what is going on. The compiler is going to evaluate compile-time constant expressions just fine, leaving a single constant.
(1) Make sure the PCIe interface is actually operating at full PCIe gen3 x16 speeds. In many systems, only one or two of the PCIe slots are configured as x16. (2) Copy the data in as huge chunks as possible, to minimize overhead (3) Pin the host side memory you are transferring to/from. As a general strategy, minimize data movement between host and device. If this kernel is the only thing you are doing with the data, performance will be limited by anemic PCIe throughput.
Let the CUDA profiler guide you. Are both executables compiled with full optimization and for the correct GPU target architecture? Are the memory access patterns of the kernel optimal? Does anything change when you try restricted pointers (I doubt this makes a difference here but good practice):
Standard practice for CUDA-accelerated applications is to build fat binaries that include machine code (SASS) for all GPU architectures one needs to support, plus PTX for the latest GPU architecture. I do not use the MSVS IDE so cannot tell you where to dial in the necessary information. When you use nvcc from the command line, you would use the -gencode switch.
I am processing a 2D area of memory doing the traditional outer and inner loop, each looping over a different dimension. My original implementation took 6ms per kernel call, however after some work to convert this to use halfs which are supposedly faster (and I have had success in other kernels using them), I find it takes 37.5ms per kernel call!
If that is your actual code, the use of double-precision literal constants is going to cause the computation to be performed in double precision, which will drag down performance. You would want to add an f suffix to the constants so they have type float:
The half-precision version of the code would seem to be negatively impacted by __short2half_ru() calls. Conversions take time, they may also interfere with some compiler optimizations. Can you change Y[], and pY[] to half precision?
The Jetson Nano has compute capability 5.3 and therefore should have full FP16 throughput per section 5.4.1 of the CUDA Programming Guide. From the limited amount of code shown there does not see an obvious reason for a 6x slowdown in the half-precision version of the code. I would double-check the compiler settings (is the code built with full optimization) and see what the CUDA profiler has to say about bottlenecks in the two versions, which should provide a clue as to what is going on.
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