* L'apprentissage automatique (ou **Machine Learning**) : cela nécessite de nombreuses multiplications de matrices et vecteurs. Imaginez une énorme feuille de calcul remplie de nombres que vous multiplierez entre eux tous au même moment.

	}
	if modulus.Cmp(priv.N) != 0 {
		return errors.New("crypto/rsa: invalid modulus")
	}

	// Check that de ≡ 1 mod p-1, for each prime.
	// This implies that e is coprime to each p-1 as e has a multiplicative
	// inverse. Therefore e is coprime to lcm(p-1,q-1,r-1,...) =
	// exponent(ℤ/nℤ). It also implies that a^de ≡ a mod p as a^(p-1) ≡ 1
	// mod p. Thus a^de ≡ a mod n for all a coprime to n, as required.

* **Machine Learning**: it normally requires lots of "matrix" and "vector" multiplications. Think of a huge spreadsheet with numbers and multiplying all of them together at the same time.

	MOVD	res+0(FP), res_ptr
	MOVD	in+8(FP), a_ptr

	MOVD	p256const0<>(SB), const0
	MOVD	p256const1<>(SB), const1

	LDP	0*16(a_ptr), (acc0, acc1)
	LDP	1*16(a_ptr), (acc2, acc3)
	// Only reduce, no multiplications are needed
	// First reduction step
	ADDS	acc0<<32, acc1, acc1
	LSR	$32, acc0, t0
	MUL	acc0, const1, t1
	UMULH	acc0, const1, acc0
	ADCS	t0, acc2
	ADCS	t1, acc3
	ADC	$0, acc0

	s := uint(0)
	for 1<<s < x {
		s++
	}
	return 1 << s
}

// Called from runtime·morestack when more stack is needed.
// Allocate larger stack and relocate to new stack.
// Stack growth is multiplicative, for constant amortized cost.
//
// g->atomicstatus will be Grunning or Gscanrunning upon entry.
// If the scheduler is trying to stop this g, then it will set preemptStop.
//

	MOVQ res+0(FP), res_ptr
	MOVQ in+8(FP), x_ptr

	MOVQ (8*0)(x_ptr), acc0
	MOVQ (8*1)(x_ptr), acc1
	MOVQ (8*2)(x_ptr), acc2
	MOVQ (8*3)(x_ptr), acc3
	XORQ acc4, acc4

	// Only reduce, no multiplications are needed
	// First stage
	MOVQ acc0, AX
	MOVQ acc0, t1
	SHLQ $32, acc0
	MULQ p256const1<>(SB)
	SHRQ $32, t1
	ADDQ acc0, acc1
	ADCQ t1, acc2
	ADCQ AX, acc3
	ADCQ DX, acc4