pml | Python Minecraft Launcher - support forge , all version | Runtime Evironment library

it is not a trouble-free path, but you can use matlab engine, see examples here

https://www.mathworks.com/help/matlab/matlab_external/calling-matlab-software-from-a-c-application.html

basically, you call engEvalString() to run matlab commands inside an invisible matlab session in the backend.

if you just need a result, you can use system calls (ShellExecute orShellExecuteEx) and call

/path/to/matlab -nojvm -nodesktop < /path/to/yourscript.m > cmdoutput.txt

to invoke a matlab session.

There are plenty of reasons why a code can be slower with -march=native, although this is quite exceptional.

That being said, in your specific context, one possible scenario is the use of slower SIMD instructions, or more precisely different SIMD instructions finally making the program slower. Indeed, GCC vectorize loops with -O3 using the SSE instruction set on x86 processors such as yours (for backward compatibility). With -march=native, GCC will likely vectorize loops using the more advanced and more recent AVX instruction set (supported by your processor but not on many old x86 processors). While the use of AVX instruction should speed your program up, it is not always the case in few pathological cases (less efficient code generated by compiler heuristics, loops are too small to leverage AVX instructions, missing/slower AVX instructions available in SSE, alignment, transition penality, energy/frequency impact, etc.).

My guess is your program is memory bound and thus AVX instructions do not make your program faster.

You can confirm this hypothesis by enabling AVX manually using -mavx -mavx2 rather than -march=native and look if your performance issue is still there. I advise you to carefully benchmark your application using a tool like perf.

You asked:

How To sort in ascending order by timestamp without "ORDER BY" in SQL?

You don't. But, you can try adding the following index, which, if used, might let SQL do the ORDER BY sort much faster:

You are almost there, the following code should work:

Try either File.ReadLines with Linq or File.ReadAllLines with Count property.

File.ReadLines

NCO identifies kd_490_bias and kd_490_rmsd as associated variables because of the ancillary_variables attribute in kd_490:

Thanks for updating the example. There's a few issues here.

First, for "err" and "err_glob". At the beginning of the loop, you set "err=0" on the host but don't update it on the device. Then after the MPI_AllReduce call, you set "err=err_glob", again on the host, so need to update "err_glob".

The second issue is that the code is getting partially present errors for "y" when run with multiple ranks. The problem being you're using the global size not the local size for "x" and "y" so when you copy "y" it overlaps with "x" due to the offsets. I fixed this by copying "xg" and "yg" to the device instead.

As for performance relative to the CPU, the main problem here is that the size is small so the code severly under utilizes the GPU. I increased the GLOB sizes to 4096 and see better relative performance, though the code converges much faster.

I also took the liberty of adding some boiler plate code that I use for rank to device assignment so the code can take advantage of multiple GPUs.

You want to PIVOT

Before doing this, assign a row_number() starting at one for each product code (partition by product_code), sorted according to however you want to prioritize the labels.

In the pivot clause take the min (or max) of the labels and placements, for each of the possible number of entries for a product:

This was an issue in the 20.7 release when adding UCX support. You can lower the optimization level to -O1 work around the problem, or update your NV HPC compiler version to 20.9 where we've resolved the issue.

https://developer.nvidia.com/nvidia-hpc-sdk-version-209-downloads