x86 Paging Tutorial Bibliography Updated 2025-07-16
Free:
- rutgers-pxk-416 chapter "Memory management: lecture notes"
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x86 Paging Tutorial Other architectures Updated 2025-07-16
Peter Cordes mentions that some architectures like MIPS leave paging almost completely in the hands of software: a TLB miss runs an OS-supplied function to walk the page tables, and insert the new mapping into the TLB. In such architectures, the OS can use whatever data structure it wants.
Quote by Bill Gates Updated 2025-07-16
SymPy Updated 2025-07-16
It also has serious applications obviously. www.sympy.org/scipy-2017-codegen-tutorial/ mentions code generation capabilities, which sounds super cool!
Let's start with some basics. fractions:outputs:Note that this is an exact value, it does not get converted to floating-point numbers where precision could be lost!
from sympy import *
sympify(2)/3 + sympify(1)/2
7/6
We can also do everything with symbols:outputs:We can now evaluate that expression object at any time:outputs:
from sympy import *
x, y = symbols('x y')
expr = x/3 + y/2
print(expr)
x/3 + y/2
expr.subs({x: 1, y: 2})
4/3
How about a square root?outputs:so we understand that the value was kept without simplification. And of course:outputs outputs:gives:
x = sqrt(2)
print(x)
sqrt(2)
sqrt(2)**2
2
. Also:sqrt(-1)
I
I
is the imaginary unit. We can use that symbol directly as well, e.g.:I*I
-1
Let's do some trigonometry:gives:and:gives:The exponential also works:gives;
cos(pi)
-1
cos(pi/4)
sqrt(2)/2
exp(I*pi)
-1
Now for some calculus. To find the derivative of the natural logarithm:outputs:Just read that. One over x. Beauty. And now for some integration:outputs:OK.
from sympy import *
x = symbols('x')
print(diff(ln(x), x))
1/x
print(integrate(1/x, x))
log(x)
Let's do some more. Let's solve a simple differential equation:Doing:outputs:which means:To be fair though, it can't do anything crazy, it likely just goes over known patterns that it has solvers for, e.g. if we change it to:it just blows up:Sad.
y''(t) - 2y'(t) + y(t) = sin(t)
from sympy import *
x = symbols('x')
f, g = symbols('f g', cls=Function)
diffeq = Eq(f(x).diff(x, x) - 2*f(x).diff(x) + f(x), sin(x)**4)
print(dsolve(diffeq, f(x)))
Eq(f(x), (C1 + C2*x)*exp(x) + cos(x)/2)
diffeq = Eq(f(x).diff(x, x)**2 + f(x), 0)
NotImplementedError: solve: Cannot solve f(x) + Derivative(f(x), (x, 2))**2
Let's try some polynomial equations:which outputs:which is a not amazingly nice version of the quadratic formula. Let's evaluate with some specific constants after the fact:which outputsLet's see if it handles the quartic equation:Something comes out. It takes up the entire terminal. Naughty. And now let's try to mess with it:and this time it spits out something more magic:Oh well.
from sympy import *
x, a, b, c = symbols('x a b c d e f')
eq = Eq(a*x**2 + b*x + c, 0)
sol = solveset(eq, x)
print(sol)
FiniteSet(-b/(2*a) - sqrt(-4*a*c + b**2)/(2*a), -b/(2*a) + sqrt(-4*a*c + b**2)/(2*a))
sol.subs({a: 1, b: 2, c: 3})
FiniteSet(-1 + sqrt(2)*I, -1 - sqrt(2)*I)
x, a, b, c, d, e, f = symbols('x a b c d e f')
eq = Eq(e*x**4 + d*x**3 + c*x**2 + b*x + a, 0)
solveset(eq, x)
x, a, b, c, d, e, f = symbols('x a b c d e f')
eq = Eq(f*x**5 + e*x**4 + d*x**3 + c*x**2 + b*x + a, 0)
solveset(eq, x)
ConditionSet(x, Eq(a + b*x + c*x**2 + d*x**3 + e*x**4 + f*x**5, 0), Complexes)
Let's try some linear algebra.Let's invert it:outputs:
m = Matrix([[1, 2], [3, 4]])
m**-1
Matrix([
[ -2, 1],
[3/2, -1/2]])
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The first two that you should study are:
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How Microwaves Work by National MagLab (2017)
Source. A bit meh. Does not mention the word cavity magnetron!How a Microwave Oven Works by EngineerGuy
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