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This page has been set up as a collaboration and design specification for the proposal to include vectorization support in the x86 and x86_64 variant of the FPC (using SSE or AVX to reduce the number of instructions, and hence the execution speed, required to encode functionality). Eventually this page can be converted into a guide to the optimizer once vectorization is at least partially supported.

CuriousKit (talk) 22:52, 11 December 2017 (CET)

vector types

As of 2017-12-12, only static arrays of singles or doubles are considered vector types, but they are unaligned unless enforced by additional compiler directives. Dynamic arrays and pointers to singles or doubles are currently not considered to be vectors.

loop optimization

Coupled with loop unrolling, it is possible to theoretically reduce the number of cycles by a factor of four or eight, depending on if SSE2 or AVX2 is used. It depends on the code contained within the loop, but if it utilizes only relatively simple arithmetic and pointer movement (e.g. reading and writing sequentially from and to an array), then it might be vectorized with relatively great ease.

For example:

	input0, input1, output: array of cardinal;
	x: integer;

	assert(length(input0) = length(input1));
	setLength(output, length(input0));
	for x := 0 to length(input0)-1 do
		output[x] := input0[x] * input1[x];

While the size of the inputs is not immediately known, it can be seen that the array sizes do not change within the loop and hence length(input0) is constant. Visualizing how the compiler might evaluate the code, it could be internally changed to the following:

	input0, input1, output: array of cardinal;
	x, arrayLen: integer;

	assert(length(input0) = length(input1));
	setLength(output, length(input0));
	arrayLen := length(input0) div 4;
	for x := 0 to arrayLen - 1 do
		output[4 * x + 0] := input0[4 * x + 0] * input1[4 * x + 0];
		output[4 * x + 1] := input0[4 * x + 1] * input1[4 * x + 1];
		output[4 * x + 2] := input0[4 * x + 2] * input1[4 * x + 2];
		output[4 * x + 3] := input0[4 * x + 3] * input1[4 * x + 3];
	// Handle leftover array entries individually
	// (count = length(input0) mod 4).

As such, the four statements in the converted for-loop can be easily assembled into SSE2 opcodes (although pmulld is SSE4.1), even with potentially unaligned memory. For example:

	lea r8,  output[0]           ; r8  := @output
	lea r9,  input0[0]           ; r9  := @input0
	lea r10, input1[0]           ; r10 := @input1
	mov ecx, arrayLen            ; ecx := arrayLen
	xor rbx, rbx                 ; rbx := 0
	; do not enter loop with empty array
	test ecx, ecx                ; ecx = 0 ?
	jz @loop_exit                ; if ecx = 0 then goto exit
	movdqu xmm0, [r9  + rbx * 4] ; xmm0 := (r9+4rbx)^
	pmulld xmm0, [r10 + rbx * 4] ; xmm0 := xmm0[0..1] * (r10+4rbx)^[0..1]
	movdqu [r8 * rbx * 4], xmm0  ; ???
	inc rbx                      ; inc(rbx)
	loop @vectorized_loop        ; dec(ecx); if ecx <> 0 then goto loop

Further optimizations can be achieved by, for example, performing two reads, multiplies and writes per loop cycle, taking advantage of the fact that modern processors have more than one SIMD port to send instructions to.

see also

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