Windows 10 vs. Ubuntu 19.10 vs. Clear Linux Performance On The Dell Ice Lake Laptop



Last month I posted benchmarks looking at the Windows 10 vs. Linux OpenGL and Vulkan graphics performance for the Ice Lake "Gen11" graphics. But for those wondering about the CPU/system performance between Windows and Linux for the Core i7-1065G7 with the Dell XPS 7390, here are those benchmarks as we compare the latest Windows 10 to Ubuntu 19.10 and Intel's own Clear Linux platform.
Also: Virtual KMS Driver To Work On Virtual Refresh Rate Support (FreeSync)
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Best Linux Distribution for Windows Users in 2019
It wasn’t too long ago that we published an article on the best Linux distros that looks like MacOS. Today, our focus is not necessarily on distributions that have a similar UI to that of Windows, but ones that are, firstly, convenient for Windows users to use due to familiarity, and secondly, without technical hurdles during installation or application set up.
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When my editor approached me to write an article on reasons to switch from Windows to Linux, I could not help but to chuckle to myself. Earlier in the week, a photographer friend of mine posted on my Facebook page thanking me for turning him on to Linux. I was hardly surprised to see his post.
I always preach the “gospel of Linux” to my friends, family, acquaintances, and, well, pretty much whoever I talk to. What is surprising is I’ve not seen my friend in over five years. I barely even remember the conversation. However, I don’t doubt it happened. I’ve been trying to turn Windows users into Linux users for well over 15 years.
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Benchmark results on mdds multi_type_vector
One of the data structures included in mdds, called multi_type_vector, stores values of different types in a single logical vector. LibreOffice Calc is one primary user of this. Calc uses this structure as its cell value store, and each instance of this value store represents a single column instance.
Internally, multi_type_vector creates multiple element blocks which are in turn stored in its parent array (primary array). This primary array maps a logical position of a value to the actual block instance that stores it. Up to version 1.5.0, this mapping process involved a linear search that always starts from the first block of the primary array. This was because each element block, though it stores the size of the block, does not store its logical position. So the only way to find the right element block that intersects the logical position of a value is to scan from the first block then keep accumulating the sizes of the encountered blocks.
The reason for not storing the logical positions of the blocks was to avoid having to update them after shifting the blocks after value insertion, which is quite common when editing spreadsheet documents.
Of course, sometimes one has to perform repeated searches to access a number of element values across a number of element blocks, in which case, always starting the search from the first block, or block 0, in every single search can be prohibitively expensive, especially when the vector is heavily fragmented.
To alleviate this, multi_type_vector provides the concept of position hints, which allows the caller to start the search from block N where N > 0. Most of multi_type_vector’s methods return a position hint which can be used for the next search operation. This allows the caller to chain all necessary search operations in such a way to only scan the primary array once for the entire sequence of search operations. The only prerequisite is that access to the elements occur in perfect ascending order. For the most part, this approach worked quite well.
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