{name: "SLTIU", argLength: 1, reg: gp11, asm: "SLTIU", aux: "Int64"}, // arg0 < auxint, unsigned, result is 0 or 1

		// Round ops to block fused-multiply-add extraction.
		{name: "LoweredRound32F", argLength: 1, reg: fp11, resultInArg0: true},
		{name: "LoweredRound64F", argLength: 1, reg: fp11, resultInArg0: true},

		// Calls

* **Deep Learning**: this is a sub-field of Machine Learning, so, the same applies. It's just that there is not a single spreadsheet of numbers to multiply, but a huge set of them, and in many cases, you use a special processor to build and / or use those models.

### Concurrency + Parallelism: Web + Machine Learning

		p.firstProg = prog
	} else {
		p.lastProg.Link = prog
	}
	p.lastProg = prog
	if doLabel {
		p.pc++
		for _, label := range p.pendingLabels {
			if p.labels[label] != nil {
				p.errorf("label %q multiply defined", label)
				return
			}
			p.labels[label] = prog
		}
		p.pendingLabels = p.pendingLabels[0:0]
	}
	prog.Pc = p.pc
	if *flags.Debug {
		fmt.Println(p.lineNum, prog)
	}
	if testOut != nil {

    bool is_signed, bool narrow_range, bool legacy_float_scale = false,
    bool use_fake_quant_num_bits = false);

// Returns the quantized type of a bias input, given the quantized types of
// other operands which are multiply-accumulated (the bias is added to the
// accumulated value).
quant::QuantizedType GetUniformQuantizedTypeForBias(
    const std::vector<quant::QuantizedType>& op_types, int adjusted_quant_dim,

(Sub32F ...) => (FSUBS ...)
(Sub64F ...) => (FSUB ...)

(Min(32|64)F x y) && buildcfg.GOPPC64 >= 9 => (XSMINJDP x y)
(Max(32|64)F x y) && buildcfg.GOPPC64 >= 9 => (XSMAXJDP x y)

// Combine 64 bit integer multiply and adds
(ADD l:(MULLD x y) z) && buildcfg.GOPPC64 >= 9 && l.Uses == 1 && clobber(l) => (MADDLD x y z)

(Mod16 x y) => (Mod32 (SignExt16to32 x) (SignExt16to32 y))
(Mod16u x y) => (Mod32u (ZeroExt16to32 x) (ZeroExt16to32 y))

	/* Vector multiply */
	{as: AVMULESB, a1: C_VREG, a2: C_VREG, a6: C_VREG, type_: 82, size: 4},              /* vector multiply, vx-form */
	{as: AVPMSUM, a1: C_VREG, a2: C_VREG, a6: C_VREG, type_: 82, size: 4},               /* vector polynomial multiply & sum, vx-form */
	{as: AVMSUMUDM, a1: C_VREG, a2: C_VREG, a3: C_VREG, a6: C_VREG, type_: 83, size: 4}, /* vector multiply-sum, va-form */

	/* Vector rotate */

	AESMC	B0.B16, B0.B16
initEncFinish:
	VLD1	(KS), [T0.B16, T1.B16, T2.B16]
	AESE	T0.B16, B0.B16
	AESMC	B0.B16, B0.B16
	AESE	T1.B16, B0.B16
	VEOR	T2.B16, B0.B16, B0.B16

	VREV64	B0.B16, B0.B16

	// Multiply by 2 modulo P
	VMOV	B0.D[0], I
	ASR	$63, I
	VMOV	I, T1.D[0]
	VMOV	I, T1.D[1]
	VAND	POLY.B16, T1.B16, T1.B16
	VUSHR	$63, B0.D2, T2.D2
	VEXT	$8, ZERO.B16, T2.B16, T2.B16
	VSHL	$1, B0.D2, B0.D2

			res[i] = &di
		}
	}
	return res
}

// hasSpaceFor returns whether the disks in `di` have space for and object of a given size.
func hasSpaceFor(di []*DiskInfo, size int64) (bool, error) {
	// We multiply the size by 2 to account for erasure coding.
	size *= 2
	if size < 0 {
		// If no size, assume diskAssumeUnknownSize.
		size = diskAssumeUnknownSize
	}

	var available uint64
	var total uint64

	// The way we calculate is as follows:
	// 1. Sum all timeouts across all KMS plugins. (check kmsPrefixTransformer for differences between v1 and v2)
	// 2. Multiply that by 2 (to allow for some buffer)
	// The reason we sum all timeout is because kmsHealthChecker() will run all health checks serially
	return &EncryptionConfiguration{
		Transformers:              transformers,

	MOVW	x+0(FP), R0
	CALL	runtime·usplitR0(SB)
	MOVW	R0, q+4(FP)
	MOVW	R1, r+8(FP)
	RET

// R0, R1 = R0/1000000, R0%1000000
TEXT runtime·usplitR0(SB),NOSPLIT,$0
	// magic multiply to avoid software divide without available m.
	// see output of go tool compile -S for x/1000000.
	MOVW	R0, R3
	MOVW	$1125899907, R1
	MULLU	R1, R0, (R0, R1)
	MOVW	R0>>18, R0
	MOVW	$1000000, R1
	MULU	R0, R1