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mysql – Which configuration to tune to best-utilize fast SSD

I am doing some testing on the MySQL performance on top of different devices, including SMR-HDD, SAS-HDD, SATA-SSD, NVMe-SSD, Optane-SSD. I want to find configurations that exclusively friendly to specific type(s) of device(s) with respect to performance.

I am using TPC-H benchmark as workload. Could you please suggest some candidate configurations to test?


performance – C++ Fast Fourier transform

This is a very simple FFT, I am wondering what I can do to make this faster and more memory efficient from the programming side (better data types, and maybe some tricks like unrolling loops or using the preprocessor if that is useful here), and not by using a more efficient mathematical algorithm. Obviously I would appreciate advice on best practices as well.

#include <stdio.h>
#include <vector>
#include <iostream>
#include <complex>
#include <cmath>
#include <algorithm>

#define N 1048576
#define PI 3.14159265358979323846

Creating the table of all N'th roots of unity.
We use notation omega_k = e^(-2 pi i / n).
template<typename U>
std::vector< std::complex<U> > rootsOfUnityCalculator() {
    std::vector< std::complex<U> > table;

    for (size_t k = 0; k < N; k++) {
        std::complex<U> kthRootOfUnity(std::cos(-2.0 * PI * k / N), std::sin(-2.0 * PI * k / N));

    return table;

Fast Fourier transform, T is the precision level, so float or double.
table is a look up table of the roots of unity. Overwrites the input.
For now only works for N a power of 2.
template<typename T>
void FFT(std::complex<T>* input, const std::vector< std::complex<T> >& table, size_t n) {

    if (n % 2 == 0) {
        // Split up the input in even and odd components
        std::complex<T>* evenComponents = new std::complex<T>(n/2);
        std::complex<T>* oddComponents = new std::complex<T>(n/2);

        for (size_t k = 0; k < n/2; k++) {
            evenComponents(k) = input(2 * k);
            oddComponents(k) = input(2 * k + 1);

        // Transform the even and odd input
        FFT(evenComponents, table, n/2);
        FFT(oddComponents, table, n/2);

        // Use the algorithm from Danielson and Lanczos
        for (size_t k = 0; k < n/2; k++) {
            std::complex<T> plusMinus = table(N / n * k) * oddComponents(k); // omega_n^k = (omega_N^(N/n))^k = omega_N^(Nk/n)
            input(k) = evenComponents(k) + plusMinus;
            input(k + n/2) = evenComponents(k) - plusMinus;

        delete() evenComponents;
        delete() oddComponents;

    } else {
        // The Fourier transform on one element does not do anything, so
        // nothing needed here.

int main() {
    std::complex<double>* input = new std::complex<double>(N);

    for (size_t k = 0; k < N; k++) {
        input(k) = k;

    const std::vector< std::complex<double> > table = rootsOfUnityCalculator<double>();

    // Overwrites the input with its Fourier transform
    FFT<double>(input, table, N);

    delete() input;

    return 0;

3d – Fast self collision/intersection detection algorithm/library for tetrahedral meshes?

I want to play with deformation of tetrahedral mesh (soft-body simulation) but i don’t want to implement self-collision detection stuff manually. Can anyone suggest me a library for this problem? I found SOFA collision detection but i’m not sure that it fits for self-intersection of tet mesh.

If there are no good library for this problem, can anyone suggest me good algorithm for self-collision detection? As far as i can understand, something like BVH of tetrahedra can help me, but it would be great if somebody with expertise shows me right direction

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performance tuning – Fast Evaluation of a series of dot product


I have a function that depends on 3 real variables x,y and z and that is defined by a series of matrix products. The evaluation of f for a specific (x,y,z) is fast ~0.02 sec but I want to evaluate the function on a huge number of points (a regularly spaced grid of x,y and z values) which in the end makes the evaluation really slow if not unmanageable. I have already tried what was proposed in this answer, but my function is not compilable, and ParalellTable is faster that vectorizing on my laptop.


For the sake of simplicity let me illustrate this with

weight = RandomReal(1, 200);
pts = RandomReal(1, 200);
M = RandomReal(1, {200, 200});
f(x_,y_) = (weight*Exp(-pts*x)).Exp(M).(weight*Exp(-pts*y))//N

How would one make the evaluation of f on multiple couples of (x,y) faster than relying on ParallelTable ?

ans = ParallelTable(f(x,y),{x,Range(100)},{y,Range(100)})

Thanks a lot for your help!

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sql server – SQL 2019 Linux, slow at GUI fast at sqlcmd/Query Analyzer

Server information

OS Redhat 7
Memory 100GB
CPU usage 1%
User db size 2 gb each on avg
total 7 user databases
SSMS installed on a jump server
Azure Data studio is also installed
App Gui on application server
Latency between jump server to db server <1ms
Latency between application server and db server <1ms
all are in same network, only switch is between them
all are physical machines


  1. SSMS – When I connect to SSMS, the databases in object explorer takes around 30 seconds to populate. Opening database properties takes upto 50 seconds to open
    Query analyser/sqlcmd from SSMS runs “select name from sys.sysdatabases” instantly without delay

  2. Azure data studio – Populates Databases list fairly quickly but takes more than a minute to show database properties.

  3. App Gui – also have the same behaviour as SSMS.

From every location, querying seems fast enough but GUI is found to be slow.

Different scenario

I have other SQL Server on windows OS, the SSMS, Azure data studio behaviour is usual where they populate information on GUI instantly.
We also have few database on Azure SQL Database and Azure VM, they also connect instantly and populate information instantly at SSMS GUI

What we want to achieve?

We need our App gui, SSMS to perform as fast as SQL Server on Windows OS at gui level.

So what could be the issue and how I can fix this?

Please help!