Speedup | 多组件打包编译加速，专为项目下library过多所设计。

I was reading an article on draw call buffers in game engines: https://realtimecollisiondetection.net/blog/?p=86

Such a buffer holds the draw calls before they are submitted to the GPU and the buffer is usually sorted before submitting according to multiple values (depth, material, viewport, etc.).

The approach in the article suggests to store the draw calls under keys that provide context such as viewport, material and depth using values packed together such that the sort order starts at the MSB and ends at LSB. Eg. first 2 bits is viewport, next 24 bits is depth, next 10 bits is material ID and so on. It should then be easy/quick to sort afterwards when actually drawing by sorting on this key. However, I am struggling as to:

• how this is implemented in practice,
• the actual speedup over simply having a struct with multiple members and implementing a comparison function that compares each of the struct members.

I have a certain base of Python code (a Flask server). I need the server to perform a performance-critical operation, which I've decided to implement in C++. However, as the C++ part has other dependencies, trying out ctypes and boost.python yielded no results (not found symbols, other libraries even when setting up the environment, etc., basically, there were problems). I believe a suitable alternative would be for me to just compile the C++ part into an executable (a single function/procedure is required) and run it from python using commands or subprocess, communicating through stdin/out for example. The only thing I'm worried about is that this will slow down the procedure enough to matter and since I'm unable to create a shared object library, calling its function from python directly, I cannot benchmark the speedup.

When I compile the C++ code into an executable and run it with some sample data, the program takes ~5s to run. This does not account for opening the process from python, nor for passing data between the processes.

The question is: How big of a speedup can one expect by using ctypes/boost with a SO compared to creating a new process to run the procedure? If I regard the number to be big enough, it would motivate me to solve the encountered problems, basically, I'm asking if it's worth it.

i am trying to build a IOS IPA for generic device, followed all the instructions, added signing certificates, team etc. but i am unable to build the product. any one please help me to resolve this issue.

here is my signing config. checked to automatically managed. added device in developer site.

sent 435785657 bytes received 92 bytes 58104766.53 bytes/sec total size is 435732165 speedup is 1.00 Warning: unable to build chain to self-signed root for signer "Apple Development: ********" /Users/Saif/Library/Developer/Xcode/DerivedData/Runner-bemaxobcrmqabgcgltuauohrwrje/Build/Intermediates.noindex/ArchiveIntermediates/Runner/InstallationBuildProductsLocation/Applications/myapp.app/Frameworks/App.framework/App: errSecInternalComponent Command PhaseScriptExecution failed with a nonzero exit code

i am just stuck on this error for about 3 days. tried each and every solution available on stackoverflow and apple developer stack.

Flutter : 2.0.1 Xcode : 11.2.1

The bottleneck of my code is the repeated calling of pow(base,exponent,modulus) for very large integers (numpy does not support such large integers, about 100 to 256 bits). However, my exponent and modulus is always the same. Could I somehow utilize this to speed the computation up with a custom function? I tried to define a function as seen below (function below is for general modulus and exponent).

However, even if I hardcode every operation without the while-loop and if-statements for a fixed exponent and modulus, it is slower than pow.

So I've been experiencing with Colab in order to conduct my deep learning project for my Bachelor's. When I run the provided example on colab to test the comparison speed between cpu and gpu it works fine, however when I try with my own code, I get the same run time for both. The task that I was conducting was simply converting 1000 jpg images to RGB values using the PIL.Image package. Shouldn't the runtime when using a gpu be much faster? Or is that only the case when running deep learning models? Please find the code I used below:

I currently encounter huge overhead because of NumPy's transpose function. I found this function virtually always run in single-threaded, whatever how large the transposed matrix/array is. I probably need to avoid this huge time cost.

To my understanding, other functions like np.dot or vector increment would run in parallel, if numpy array is large enough. Some element-wise operations seems to be better parallelized in package numexpr, but numexpr probably couldn't handle transpose.

I wish to learn what is the better way to resolve problem. To state this problem in detail,

• Sometimes NumPy runs transpose ultrafast (like B = A.T) because the transposed tensor is not used in calculation or be dumped, and there is no need to really transpose data at this stage. When calling B[:] = A.T, that really do transpose of data.
• I think a parallelized transpose function should be a resolution. The problem is how to implement it.
• Hope the solution does not require packages other than NumPy. ctype binding is acceptable. And hope code is not too difficult to use nor too complicated.
• Tensor transpose is a plus. Though techniques to transpose a matrix could be also utilized in specific tensor transpose problem, I think it could be difficult to write a universal API for tensor transpose. I actually also need to handle tensor transpose, but handling tensors could complicate this stackoverflow problem.
• And if there's possibility to implement parallelized transpose in future, or there's a plan exists? Then there would be no need to implement transpose by myself ;)

Thanks in advance for any suggestions!

Handling a model transpose problem (size of A is ~763MB) on my personal computer in Linux with 4-cores available (400% CPU in total).

My PyQt app is slow. What have i do to speedup this def:

I was reading Chapter 8 of the "Modern C++ Programming Cookbook, 2nd edition" on concurrency and stumbled upon something that puzzles me.

The author implements different versions of parallel map and reduce functions using std::thread and std::async. The implementations are really close; for example, the heart of the parallel_map functions are

I'm trying to add some patches to the llvm Opam package, but I'm having issues testing it because it seems like running opam install . from the package root ignores the url section and doesn't download & decompress the source archive, thus failing when applying patches.

This is the opam file for reference: