duality | complete application demo | Object-Relational Mapping library

According to Computer Architecture: A Quantitative Approach, vector processors, both classic ones like Cray and modern ones like Nvidia, provide gather/scatter to improve performance on sparse arrays, where the array is in sparse form in memory and gathered to dense form in vector registers.

It seems to me if the array is so sparse – the density of nonzero elements so low – that it would be inefficient to represent it in dense form in memory, then it must also be inefficient to represent it in dense form in vector registers. For example, if 99% of entries are zero, then representing it in dense form in memory would waste 98% of the memory... but would waste 99% of the vector register capacity and arithmetic operations! That's high enough that you would be better off just processing it with scalar operations. If 75% of entries are zero, then you would be better off to represent it in dense form in memory and avoid the nonsequential gather/scatter operations.

What is the typical density of nonzero elements where gather/scatter is used? Is it very common for it to be in some sweet spot where this duality makes sense - in which case, what workloads generate this particular density range so often - or is there something else I am missing?

Let's say I have the following string:

Ok, I stripped the code to ultra minimal to illustrate the problem and make it reproducible

context :

• Python 3.7
• No VENV or funny stuff

Talk is cheap. Show you the code :

code structure :

Why do I get an overflow exception even if I apply the unchecked operator on an expression?

This is my code:

I've tried to fit in an Isotope gallery in a column with a flexible width.

Unfortunately, it only works if I change the window size of the browser.

Here is my code:

I want to code a path navigation with jQuery.

This is how it looks at the moment:

So I'm curious how to get the number of branch and bounds nodes explored. I was interested in this to gage how hard solving my IP is - if there is a better metric for this please feel free to share.

I tried to use

I often see regression tables in publications where the plain standard errors are reported (in parentheses), together with the robust standard errors (in brackets) below the plain standard errors. The tables also include the accompanying asterisks beside the parentheses/brackets indicating statistical significance.

What is the most sensible way to create regression reports like these?

So far, I have been using the estout package in Stata. For a given model, I could have one column with the plain standard error and another with the robust one.

For example, using estout, I could do the following: