assembly 基于SIMD的快速位矩阵(64x64)转置算法

h7appiyu 于 11个月前发布在其他

关注(0)|答案(4)|浏览(114)

我试图了解，如果有一个快速的方法来做一个矩阵转置（64x64位）使用ARM SIMD指令。
我试图探索ARM SIMD的VTRN指令，但不确定它在这种情况下的有效应用。
输入矩阵表示为uint64 mat[64]，输出应该是按位转置。
例如，如果输入是：

0000....
1111....
0000....
1111....

字符串
预期输出：

0101....
0101....
0101....
0101....

型

assembly

来源：https://stackoverflow.com/questions/71552776/fast-bit-matrix-64x64-transpose-algorithm-using-simd-arm

4条答案

按热度按时间

pkwftd7m1#

矩阵转置的基本递归方案是将矩阵表示为分块矩阵

AB
CD

字符串
通过首先转置A、B、C和D中的每一个，然后交换B和C来转置。在实践中，这意味着应用一系列越来越粗糙的混合步骤，首先使用逐位操作，然后使用置换操作。
这可以例如实现为as follows：

# transpose a 64x64 bit matrix held in x0
    GLOBL(xpose_asm)
FUNC(xpose_asm)
    # plan of attack: use registers v16--v32 to hold
    # half the array, v0--v7 for scratch.  First transpose
    # the two array halves individually, then swap the
    # second and third quarters.
    mov x4, lr

    mov x2, x0
    bl  NAME(xpose_half)
    mov x3, x0
    bl  NAME(xpose_half)

    # final step: transpose 64x64 bit matrices
    # we have to do this one in two parts as to not run
    # out of registers
    mov x5, x2
    mov x6, x3
    bl  NAME(xpose_final)
    bl  NAME(xpose_final)

    ret x4
ENDFUNC(xpose_asm)

    # Transpose half a 32x64 bit matrix held in x0.
    # On return, advance x0 by 32*8 = 256 byte.
FUNC(xpose_half)
    # v16 holds rows 0 and 4, v17 holds 1 and 5, and so on
    mov x1, x0
    ld4 {v16.2d, v17.2d, v18.2d, v19.2d}, [x0], #64
    ld4 {v20.2d, v21.2d, v22.2d, v23.2d}, [x0], #64
    ld4 {v24.2d, v25.2d, v26.2d, v27.2d}, [x0], #64
    ld4 {v28.2d, v29.2d, v30.2d, v31.2d}, [x0], #64

    # macro for a transposition step.  Trashes v6 and v7
.macro  xpstep lo, hi, mask, shift
    ushr v6.2d, \lo\().2d, #\shift
    shl v7.2d, \hi\().2d, #\shift
    bif \lo\().16b, v7.16b, \mask\().16b
    bit \hi\().16b, v6.16b, \mask\().16b
.endm

    # 1st step: transpose 2x2 bit matrices
    movi    v0.16b, #0x55
    xpstep  v16, v17, v0, 1
    xpstep  v18, v19, v0, 1
    xpstep  v20, v21, v0, 1
    xpstep  v22, v23, v0, 1
    xpstep  v24, v25, v0, 1
    xpstep  v26, v27, v0, 1
    xpstep  v28, v29, v0, 1
    xpstep  v30, v31, v0, 1

    # 2nd step: transpose 4x4 bit matrices
    movi    v0.16b, #0x33
    xpstep  v16, v18, v0, 2
    xpstep  v17, v19, v0, 2
    xpstep  v20, v22, v0, 2
    xpstep  v21, v23, v0, 2
    xpstep  v24, v26, v0, 2
    xpstep  v25, v27, v0, 2
    xpstep  v28, v30, v0, 2
    xpstep  v29, v31, v0, 2

    # immediate step: zip vectors to change
    # colocation.  As a side effect, every other
    # vector is temporarily relocated to the v0..v7
    # register range
    zip1    v0.2d,  v16.2d, v17.2d
    zip2    v17.2d, v16.2d, v17.2d
    zip1    v1.2d,  v18.2d, v19.2d
    zip2    v19.2d, v18.2d, v19.2d
    zip1    v2.2d,  v20.2d, v21.2d
    zip2    v21.2d, v20.2d, v21.2d
    zip1    v3.2d,  v22.2d, v23.2d
    zip2    v23.2d, v22.2d, v23.2d
    zip1    v4.2d,  v24.2d, v25.2d
    zip2    v25.2d, v24.2d, v25.2d
    zip1    v5.2d,  v26.2d, v27.2d
    zip2    v27.2d, v26.2d, v27.2d
    zip1    v6.2d,  v28.2d, v29.2d
    zip2    v29.2d, v28.2d, v29.2d
    zip1    v7.2d,  v30.2d, v31.2d
    zip2    v31.2d, v30.2d, v31.2d

    # macro for the 3rd transposition step
    # swap low 4 bit of each hi member with
    # high 4 bit of each orig member.  The orig
    # members are copied to lo in the process.
.macro  xpstep3 lo, hi, orig
    mov \lo\().16b, \orig\().16b
    sli \lo\().16b, \hi\().16b, #4
    sri \hi\().16b, \orig\().16b, #4
.endm

    # 3rd step: transpose 8x8 bit matrices
    # special code is needed here since we need to
    # swap row n row line n+4, but these rows are
    # always colocated in the same register
    xpstep3 v16, v17, v0
    xpstep3 v18, v19, v1
    xpstep3 v20, v21, v2
    xpstep3 v22, v23, v3
    xpstep3 v24, v25, v4
    xpstep3 v26, v27, v5
    xpstep3 v28, v29, v6
    xpstep3 v30, v31, v7

    # registers now hold
    # v16: { 0,  1}  v17: { 4,  5}  v18: { 2,  3}  v19: { 6,  7}
    # v20: { 8,  9}  v21: {12, 13}  v22: {10, 11}  v23: {14, 15}
    # v24: {16, 17}  v25: {20, 21}  v26: {18, 19}  v27: {22, 23}
    # v28: {24, 25}  v29: {28, 29}  v30: {26, 27}  v31: {30, 31}

    # 4th step: transpose 16x16 bit matrices
    # this step again moves half the registers to v0--v7
    trn1    v0.16b,  v16.16b, v20.16b
    trn2    v20.16b, v16.16b, v20.16b
    trn1    v1.16b,  v17.16b, v21.16b
    trn2    v21.16b, v17.16b, v21.16b
    trn1    v2.16b,  v18.16b, v22.16b
    trn2    v22.16b, v18.16b, v22.16b
    trn1    v3.16b,  v19.16b, v23.16b
    trn2    v23.16b, v19.16b, v23.16b
    trn1    v4.16b,  v24.16b, v28.16b
    trn2    v28.16b, v24.16b, v28.16b
    trn1    v5.16b,  v25.16b, v29.16b
    trn2    v29.16b, v25.16b, v29.16b
    trn1    v6.16b,  v26.16b, v30.16b
    trn2    v30.16b, v26.16b, v30.16b
    trn1    v7.16b,  v27.16b, v31.16b
    trn2    v31.16b, v27.16b, v31.16b

    # 5th step: transpose 32x32 bit matrices
    # while we are at it, shuffle the order of
    # entries such that they are in order
    trn1    v16.8h, v0.8h, v4.8h
    trn2    v24.8h, v0.8h, v4.8h
    trn1    v18.8h, v1.8h, v5.8h
    trn2    v26.8h, v1.8h, v5.8h
    trn1    v17.8h, v2.8h, v6.8h
    trn2    v25.8h, v2.8h, v6.8h
    trn1    v19.8h, v3.8h, v7.8h
    trn2    v27.8h, v3.8h, v7.8h

    trn1    v0.8h, v20.8h, v28.8h
    trn2    v4.8h, v20.8h, v28.8h
    trn1    v2.8h, v21.8h, v29.8h
    trn2    v6.8h, v21.8h, v29.8h
    trn1    v1.8h, v22.8h, v30.8h
    trn2    v5.8h, v22.8h, v30.8h
    trn1    v3.8h, v23.8h, v31.8h
    trn2    v7.8h, v23.8h, v31.8h

    # now deposit the partially transposed matrix
    st1 {v16.2d, v17.2d, v18.2d, v19.2d}, [x1], #64
    st1 {v0.2d, v1.2d, v2.2d, v3.2d}, [x1], #64
    st1 {v24.2d, v25.2d, v26.2d, v27.2d}, [x1], #64
    st1 {v4.2d, v5.2d, v6.2d, v7.2d}, [x1], #64

    ret
ENDFUNC(xpose_half)

FUNC(xpose_final)
    ld1 {v16.2d, v17.2d, v18.2d, v19.2d}, [x2], #64
    ld1 {v24.2d, v25.2d, v26.2d, v27.2d}, [x3], #64
    ld1 {v20.2d, v21.2d, v22.2d, v23.2d}, [x2], #64
    ld1 {v28.2d, v29.2d, v30.2d, v31.2d}, [x3], #64

    trn1    v0.4s, v16.4s, v24.4s
    trn2    v4.4s, v16.4s, v24.4s
    trn1    v1.4s, v17.4s, v25.4s
    trn2    v5.4s, v17.4s, v25.4s
    trn1    v2.4s, v18.4s, v26.4s
    trn2    v6.4s, v18.4s, v26.4s
    trn1    v3.4s, v19.4s, v27.4s
    trn2    v7.4s, v19.4s, v27.4s

    trn1    v16.4s, v20.4s, v28.4s
    trn2    v24.4s, v20.4s, v28.4s
    trn1    v17.4s, v21.4s, v29.4s
    trn2    v25.4s, v21.4s, v29.4s
    trn1    v18.4s, v22.4s, v30.4s
    trn2    v26.4s, v22.4s, v30.4s
    trn1    v19.4s, v23.4s, v31.4s
    trn2    v27.4s, v23.4s, v31.4s

    st1 {v0.2d, v1.2d, v2.2d, v3.2d}, [x5], #64
    st1 {v4.2d, v5.2d, v6.2d, v7.2d}, [x6], #64
    st1 {v16.2d, v17.2d, v18.2d, v19.2d}, [x5], #64
    st1 {v24.2d, v25.2d, v26.2d, v27.2d}, [x6], #64

    ret
ENDFUNC(xpose_final)

型
我们可以看到，性能与Lee's approach相当，大约快三倍。

# Apple M1
name  time/op
Ref      764ns ± 0%
Lee      102ns ± 0%
Fuz     34.7ns ± 0%

name  speed
Ref    670MB/s ± 0%
Lee   5.01GB/s ± 0%
Fuz   14.7GB/s ± 0%

# Kunpeng 920
name  time/op
Ref     3.73µs ± 0%
Lee      391ns ± 1%
Fuz     96.0ns ± 0%

name  speed
Ref    137MB/s ± 0%
Lee   1.31GB/s ± 1%
Fuz   5.33GB/s ± 0%

# ARM Cortex A72
name  time/op
Ref     8.13µs ± 0%
Lee      892ns ± 0%
Fuz      296ns ± 0%

name  speed
Ref   63.0MB/s ± 0%
Lee    574MB/s ± 0%
Fuz   1.73GB/s ± 0%

# Cavium ThunderX
name  time/op
Ref     19.7µs ± 0%
Lee     1.15µs ± 0%
Fuz      690ns ± 0%

name  speed
Ref   25.9MB/s ± 0%
Lee    444MB/s ± 0%
Fuz    742MB/s ± 0%

型
进一步的改进可能是可能的。例如，可以将合适的置换掩码与tbl指令集一起使用，以一次执行多个转置步骤（特别是步骤3到5）。
请注意，该算法只需要加载和写出数组两次。一次是转置每个32x32子数组（两次调用xpose_half），另一次是交换右上角和左下角的32x32子数组。在这两种情况下，使用最大宽度64字节的加载和存储，将内存操作量降至最低。

赞(0）回复(0）举报 11个月前

nr9pn0ug2#

我做了一些调查和阅读其他答案，并实现了一些不同的方法（在Rust和组装），并在我的Apple M2 Max上进行基准测试。
tl;dr使用 neon 说明书可获得比非 neon 版本高2倍的优势。
结果如下：

recursive:                 163 ns/iter (+/- 10)
Hacker's Delight loop:     157 ns/iter (+/- 21)
Hacker's Delight unrolled:  60 ns/iter (+/- 4)
Lees's answer:              37 ns/iter (+/- 3)
fuz's answer:               31 ns/iter (+/- 1)

字符串

“递归”算法是一种基本的递归算法，使用 neon ld4指令等稍微优化，但涉及更多的内存移动。
“Hacker's Delight loop”版本是Hacker's Delight的一个非常简单的实现，图7-3（至少在第1版中是这样），但修改为对64位字进行位反转以匹配其他答案。它看起来像这样：

let mut j = 32;
 let mut m = 0xffffffffu64;
 while j != 0 {
     let mut k = 0;
     while k < 64 {
         unsafe {
             let a = *x.get_unchecked(k + j);
             let b = *x.get_unchecked(k);
             let t = (a ^ (b >> j)) & m;
             *x.get_unchecked_mut(k + j) = a ^ t;
             *x.get_unchecked_mut(k) = b ^ (t << j);
         }

         k = (k + j + 1) & !j;
     }
     j >>= 1;
     m ^= m << j;
 }

型
Rust/LLVM会自动向量化一点，但不是很多（Rust 1.76.0）。

“黑客的喜悦展开”版本是相同的，但与循环手动展开。我没有观察到任何自动矢量化。
“Lee的答案”版本改编自他的第二个答案https://stackoverflow.com/a/71653601/10524--然而，我无法获得输出正确答案的代码，这可能是我在转录它时的错误，或者在原始汇编中，但我现在不打算花更多的时间来修复它。
“fuz's answer”版本改编自https://stackoverflow.com/a/71555778/10524--这段代码可以工作。

在Hacker's Delight展开版本中可能还有更多的改进空间，可以切换到汇编/intrinsic以实现更有效的内存管理，并可能使用旋转指令进行更有效的交换（正如本书所述）。
Lee的代码将转置的比特输出到内存的一个单独区域，这是它稍微慢一点的原因之一。如果我们不做回拷，它就在fuz的答案的误差范围内。如果我们做就地转换，可能还有一些改进的空间。
福兹的回答似乎是目前的赢家。
我已经在Rust中实现了所有这些（在适用的情况下使用内联汇编）：https://github.com/swenson/binary_matrix/tree/simd-transpose/src

赞(0）回复(0）举报 11个月前

myzjeezk3#

.arch armv8-a
    .global transposeBitwise64x64_noob
    .text

.balign 64
.func
// void transposeBitwise64x64_noob(uint64_t *pDst, uint64_t *pSrc);
pDst    .req    x0
pSrc0   .req    x1
pSrc1   .req    x2
pDst1   .req    x3
stride  .req    x4
count   .req    w5

pDst0   .req    pDst

// no "battle plan" here. not needed for such a self-explanatory cakewalk
transposeBitwise64x64_noob:
    add     pSrc1, pSrc0, #64
    movi    v6.16b, #0xcc
    mov     stride, #128
    movi    v7.16b, #0xaa
    sub     pDst, pDst, #32
    mov     count, #2

.balign 16
1:
    ld4     {v16.16b, v17.16b, v18.16b, v19.16b}, [pSrc0], stride
    ld4     {v20.16b, v21.16b, v22.16b, v23.16b}, [pSrc1], stride
    ld4     {v24.16b, v25.16b, v26.16b, v27.16b}, [pSrc0], stride
    ld4     {v28.16b, v29.16b, v30.16b, v31.16b}, [pSrc1], stride

    stp     q16, q20, [pDst, #32]!
    subs    count, count, #1
    stp     q17, q21, [pDst, #1*64]
    stp     q18, q22, [pDst, #2*64]
    stp     q19, q23, [pDst, #3*64]
    stp     q24, q28, [pDst, #4*64]
    stp     q25, q29, [pDst, #5*64]
    stp     q26, q30, [pDst, #6*64]
    stp     q27, q31, [pDst, #7*64]
    b.ne    1b
    // 8x64 matrix transpose virtually finished. What a moron needs zip1/zip2/trn for that?
    nop

    sub     pSrc0, pDst, #32
    add     pSrc1, pDst, #256-32
    mov     count, #4
    sub     pDst0, pDst, #32
    add     pDst1, pSrc0, #256

1:
    // 8x64 matrix transpose finished on-the-fly while reloading. Again, who the hell needs permutation instructions when we have ld2/ld3/ld4?
    ld2     {v24.16b, v25.16b}, [pSrc0], #32
    ld2     {v26.16b, v27.16b}, [pSrc1], #32
    ld2     {v28.16b, v29.16b}, [pSrc0], #32
    ld2     {v30.16b, v31.16b}, [pSrc1], #32
    subs    count, count, #1

    // nosy noob shut up remark: the trns below aren't part of the matrix transpose
    trn1    v16.2d, v24.2d, v25.2d  // row0
    trn2    v17.2d, v24.2d, v25.2d  // row1
    trn1    v18.2d, v26.2d, v27.2d  // row2
    trn2    v19.2d, v26.2d, v28.2d  // row3
    trn1    v20.2d, v28.2d, v29.2d  // row4
    trn2    v21.2d, v28.2d, v29.2d  // row5
    trn1    v22.2d, v30.2d, v31.2d  // row6
    trn2    v23.2d, v30.2d, v31.2d  // row7

    mov     v24.16b, v16.16b
    mov     v25.16b, v17.16b
    mov     v26.16b, v18.16b
    mov     v27.16b, v19.16b

    sli     v16.16b, v20.16b, #4
    sli     v17.16b, v21.16b, #4
    sli     v18.16b, v22.16b, #4
    sli     v19.16b, v23.16b, #4
    sri     v20.16b, v24.16b, #4
    sri     v21.16b, v25.16b, #4
    sri     v22.16b, v26.16b, #4
    sri     v23.16b, v27.16b, #4

    shl     v24.16b, v18.16b, #2
    shl     v25.16b, v19.16b, #2
    ushr    v26.16b, v16.16b, #2
    ushr    v27.16b, v17.16b, #2
    shl     v28.16b, v22.16b, #2
    shl     v29.16b, v23.16b, #2
    ushr    v30.16b, v20.16b, #2
    ushr    v31.16b, v21.16b, #2

    bit     v16.16b, v24.16b, v6.16b
    bit     v17.16b, v25.16b, v6.16b
    bif     v18.16b, v26.16b, v6.16b
    bif     v19.16b, v27.16b, v6.16b
    bit     v20.16b, v28.16b, v6.16b
    bit     v21.16b, v29.16b, v6.16b
    bif     v22.16b, v30.16b, v6.16b
    bif     v23.16b, v31.16b, v6.16b

    shl     v24.16b, v17.16b, #1
    ushr    v25.16b, v16.16b, #1
    shl     v26.16b, v19.16b, #1
    ushr    v27.16b, v18.16b, #1
    shl     v28.16b, v21.16b, #1
    ushr    v29.16b, v20.16b, #1
    shl     v30.16b, v23.16b, #1
    ushr    v31.16b, v22.16b, #1

    bit     v16.16b, v24.16b, v7.16b
    bif     v17.16b, v25.16b, v7.16b
    bit     v18.16b, v26.16b, v7.16b
    bif     v19.16b, v27.16b, v7.16b
    bit     v20.16b, v28.16b, v7.16b
    bif     v21.16b, v29.16b, v7.16b
    bit     v22.16b, v30.16b, v7.16b
    bif     v23.16b, v31.16b, v7.16b

    st4     {v16.d, v17.d, v18.d, v19.d}[0], [pDst0], #32
    st4     {v16.d, v17.d, v18.d, v19.d}[1], [pDst1], #32
    st4     {v20.d, v21.d, v22.d, v23.d}[0], [pDst0], #32
    st4     {v20.d, v21.d, v22.d, v23.d}[1], [pDst1], #32
    b.ne    1b

// Everyone has a plan until they get punched in the mouth - Mike Tyson

.balign 16
    ret
.endfunc
.end

字符串
这可能是完美的新手代码：完美地最小化零延迟。
我以为福兹也会送类似的东西，但是......
尽管如此，这毕竟是一个新手版本，我会给它一个C级（befriedigend）给予。
为什么它不值得A级将在下次更新时明确：带宽限制和功耗，这是现代多核处理器的真实的问题。
Fuz's code（F级，万向轴）：

# transpose a 64x64 bit matrix held in x0
    GLOBL(xpose_asm)
FUNC(xpose_asm)
    # plan of attack: use registers v16--v32 to hold
    # half the array, v0--v7 for scratch.  First transpose
    # the two array halves individually, then swap the
    # second and third quarters.
    mov x4, lr

    mov x2, x0
    bl  NAME(xpose_half)
    mov x3, x0
    bl  NAME(xpose_half)

    # final step: transpose 64x64 bit matrices
    # we have to do this one in two parts as to not run
    # out of registers
    mov x5, x2
    mov x6, x3
    bl  NAME(xpose_final)
    bl  NAME(xpose_final)

    ret x4
ENDFUNC(xpose_asm)

    # Transpose half a 32x64 bit matrix held in x0.
    # On return, advance x0 by 32*8 = 256 byte.
FUNC(xpose_half)
    # v16 holds rows 0 and 4, v17 holds 1 and 5, and so on
    mov x1, x0
    ld4 {v16.2d, v17.2d, v18.2d, v19.2d}, [x0], #64
    ld4 {v20.2d, v21.2d, v22.2d, v23.2d}, [x0], #64
    ld4 {v24.2d, v25.2d, v26.2d, v27.2d}, [x0], #64
    ld4 {v28.2d, v29.2d, v30.2d, v31.2d}, [x0], #64

    # macro for a transposition step.  Trashes v6 and v7
.macro  xpstep lo, hi, mask, shift
    ushr v6.2d, \lo\().2d, #\shift
    shl v7.2d, \hi\().2d, #\shift
    bif \lo\().16b, v7.16b, \mask\().16b
    bit \hi\().16b, v6.16b, \mask\().16b
.endm

    # 1st step: transpose 2x2 bit matrices
    movi    v0.16b, #0x55
    xpstep  v16, v17, v0, 1
    xpstep  v18, v19, v0, 1
    xpstep  v20, v21, v0, 1
    xpstep  v22, v23, v0, 1
    xpstep  v24, v25, v0, 1
    xpstep  v26, v27, v0, 1
    xpstep  v28, v29, v0, 1
    xpstep  v30, v31, v0, 1

    # 2nd step: transpose 4x4 bit matrices
    movi    v0.16b, #0x33
    xpstep  v16, v18, v0, 2
    xpstep  v17, v19, v0, 2
    xpstep  v20, v22, v0, 2
    xpstep  v21, v23, v0, 2
    xpstep  v24, v26, v0, 2
    xpstep  v25, v27, v0, 2
    xpstep  v28, v30, v0, 2
    xpstep  v29, v31, v0, 2

    # immediate step: zip vectors to change
    # colocation.  As a side effect, every other
    # vector is temporarily relocated to the v0..v7
    # register range
    zip1    v0.2d,  v16.2d, v17.2d
    zip2    v17.2d, v16.2d, v17.2d
    zip1    v1.2d,  v18.2d, v19.2d
    zip2    v19.2d, v18.2d, v19.2d
    zip1    v2.2d,  v20.2d, v21.2d
    zip2    v21.2d, v20.2d, v21.2d
    zip1    v3.2d,  v22.2d, v23.2d
    zip2    v23.2d, v22.2d, v23.2d
    zip1    v4.2d,  v24.2d, v25.2d
    zip2    v25.2d, v24.2d, v25.2d
    zip1    v5.2d,  v26.2d, v27.2d
    zip2    v27.2d, v26.2d, v27.2d
    zip1    v6.2d,  v28.2d, v29.2d
    zip2    v29.2d, v28.2d, v29.2d
    zip1    v7.2d,  v30.2d, v31.2d
    zip2    v31.2d, v30.2d, v31.2d

    # macro for the 3rd transposition step
    # swap low 4 bit of each hi member with
    # high 4 bit of each orig member.  The orig
    # members are copied to lo in the process.
.macro  xpstep3 lo, hi, orig
    mov \lo\().16b, \orig\().16b
    sli \lo\().16b, \hi\().16b, #4
    sri \hi\().16b, \orig\().16b, #4
.endm

    # 3rd step: transpose 8x8 bit matrices
    # special code is needed here since we need to
    # swap row n row line n+4, but these rows are
    # always colocated in the same register
    xpstep3 v16, v17, v0
    xpstep3 v18, v19, v1
    xpstep3 v20, v21, v2
    xpstep3 v22, v23, v3
    xpstep3 v24, v25, v4
    xpstep3 v26, v27, v5
    xpstep3 v28, v29, v6
    xpstep3 v30, v31, v7

    # registers now hold
    # v16: { 0,  1}  v17: { 4,  5}  v18: { 2,  3}  v19: { 6,  7}
    # v20: { 8,  9}  v21: {12, 13}  v22: {10, 11}  v23: {14, 15}
    # v24: {16, 17}  v25: {20, 21}  v26: {18, 19}  v27: {22, 23}
    # v28: {24, 25}  v29: {28, 29}  v30: {26, 27}  v31: {30, 31}

    # 4th step: transpose 16x16 bit matrices
    # this step again moves half the registers to v0--v7
    trn1    v0.16b,  v16.16b, v20.16b
    trn2    v20.16b, v16.16b, v20.16b
    trn1    v1.16b,  v17.16b, v21.16b
    trn2    v21.16b, v17.16b, v21.16b
    trn1    v2.16b,  v18.16b, v22.16b
    trn2    v22.16b, v18.16b, v22.16b
    trn1    v3.16b,  v19.16b, v23.16b
    trn2    v23.16b, v19.16b, v23.16b
    trn1    v4.16b,  v24.16b, v28.16b
    trn2    v28.16b, v24.16b, v28.16b
    trn1    v5.16b,  v25.16b, v29.16b
    trn2    v29.16b, v25.16b, v29.16b
    trn1    v6.16b,  v26.16b, v30.16b
    trn2    v30.16b, v26.16b, v30.16b
    trn1    v7.16b,  v27.16b, v31.16b
    trn2    v31.16b, v27.16b, v31.16b

    # 5th step: transpose 32x32 bit matrices
    # while we are at it, shuffle the order of
    # entries such that they are in order
    trn1    v16.8h, v0.8h, v4.8h
    trn2    v24.8h, v0.8h, v4.8h
    trn1    v18.8h, v1.8h, v5.8h
    trn2    v26.8h, v1.8h, v5.8h
    trn1    v17.8h, v2.8h, v6.8h
    trn2    v25.8h, v2.8h, v6.8h
    trn1    v19.8h, v3.8h, v7.8h
    trn2    v27.8h, v3.8h, v7.8h

    trn1    v0.8h, v20.8h, v28.8h
    trn2    v4.8h, v20.8h, v28.8h
    trn1    v2.8h, v21.8h, v29.8h
    trn2    v6.8h, v21.8h, v29.8h
    trn1    v1.8h, v22.8h, v30.8h
    trn2    v5.8h, v22.8h, v30.8h
    trn1    v3.8h, v23.8h, v31.8h
    trn2    v7.8h, v23.8h, v31.8h

    # now deposit the partially transposed matrix
    st1 {v16.2d, v17.2d, v18.2d, v19.2d}, [x1], #64
    st1 {v0.2d, v1.2d, v2.2d, v3.2d}, [x1], #64
    st1 {v24.2d, v25.2d, v26.2d, v27.2d}, [x1], #64
    st1 {v4.2d, v5.2d, v6.2d, v7.2d}, [x1], #64

    ret
ENDFUNC(xpose_half)

FUNC(xpose_final)
    ld1 {v16.2d, v17.2d, v18.2d, v19.2d}, [x2], #64
    ld1 {v24.2d, v25.2d, v26.2d, v27.2d}, [x3], #64
    ld1 {v20.2d, v21.2d, v22.2d, v23.2d}, [x2], #64
    ld1 {v28.2d, v29.2d, v30.2d, v31.2d}, [x3], #64

    trn1    v0.4s, v16.4s, v24.4s
    trn2    v4.4s, v16.4s, v24.4s
    trn1    v1.4s, v17.4s, v25.4s
    trn2    v5.4s, v17.4s, v25.4s
    trn1    v2.4s, v18.4s, v26.4s
    trn2    v6.4s, v18.4s, v26.4s
    trn1    v3.4s, v19.4s, v27.4s
    trn2    v7.4s, v19.4s, v27.4s

    trn1    v16.4s, v20.4s, v28.4s
    trn2    v24.4s, v20.4s, v28.4s
    trn1    v17.4s, v21.4s, v29.4s
    trn2    v25.4s, v21.4s, v29.4s
    trn1    v18.4s, v22.4s, v30.4s
    trn2    v26.4s, v22.4s, v30.4s
    trn1    v19.4s, v23.4s, v31.4s
    trn2    v27.4s, v23.4s, v31.4s

    st1 {v0.2d, v1.2d, v2.2d, v3.2d}, [x5], #64
    st1 {v4.2d, v5.2d, v6.2d, v7.2d}, [x6], #64
    st1 {v16.2d, v17.2d, v18.2d, v19.2d}, [x5], #64
    st1 {v24.2d, v25.2d, v26.2d, v27.2d}, [x6], #64

    ret
ENDFUNC(xpose_final)

型

赞(0）回复(0）举报 11个月前

vd2z7a6w4#

数据大小远远超过寄存器组的大小。您可以选择：

跨接加载和连续存储
连续装入和跨步存储

而连续存储总是更可取的。

#include <arm_neon.h>    
void transposeBitwise64x64(uint64_t *pDst, uint64_t *pSrc)
    {
        uint8x8_t drow0, drow1, drow2, drow3, drow4, drow5, drow6, drow7;
        uint8x8_t dtmp0, dtmp1, dtmp2, dtmp3, dtmp4, dtmp5, dtmp6, dtmp7;
        uint8x16_t qrow0, qrow1, qrow2, qrow3, qrow4, qrow5, qrow6, qrow7;
        uint8x16_t qtmp0, qtmp1, qtmp2, qtmp3, qtmp4, qtmp5, qtmp6, qtmp7;
        const intptr_t sstride = 16;
        uint8_t *pSrc1, *pSrc2, *pSrcBase;
        uint32_t count = 8;
    
        drow0 = vmov_n_u8(0);
        drow1 = vmov_n_u8(0);
        drow2 = vmov_n_u8(0);
        drow3 = vmov_n_u8(0);
        drow4 = vmov_n_u8(0);
        drow5 = vmov_n_u8(0);
        drow6 = vmov_n_u8(0);
        drow7 = vmov_n_u8(0);
    
        pSrcBase = (uint8_t *) pSrc;
    
        do {
            pSrc1 = pSrcBase;
            pSrc2 = pSrcBase + 8;
            pSrcBase += 1;
            drow0 = vld1_lane_u8(pSrc1, drow0, 0); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 0); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 0); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 0); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 0); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 0); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 0); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 0); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 1); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 1); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 1); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 1); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 1); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 1); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 1); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 1); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 2); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 2); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 2); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 2); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 2); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 2); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 2); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 2); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 3); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 3); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 3); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 3); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 3); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 3); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 3); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 3); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 4); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 4); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 4); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 4); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 4); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 4); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 4); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 4); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 5); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 5); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 5); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 5); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 5); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 5); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 5); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 5); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 6); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 6); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 6); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 6); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 6); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 6); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 6); pSrc1 += sstride;
            drow7 = vld1_lane_u8(pSrc2, drow7, 6); pSrc2 += sstride;
            drow0 = vld1_lane_u8(pSrc1, drow0, 7); pSrc1 += sstride;
            drow1 = vld1_lane_u8(pSrc2, drow1, 7); pSrc2 += sstride;
            drow2 = vld1_lane_u8(pSrc1, drow2, 7); pSrc1 += sstride;
            drow3 = vld1_lane_u8(pSrc2, drow3, 7); pSrc2 += sstride;
            drow4 = vld1_lane_u8(pSrc1, drow4, 7); pSrc1 += sstride;
            drow5 = vld1_lane_u8(pSrc2, drow5, 7); pSrc2 += sstride;
            drow6 = vld1_lane_u8(pSrc1, drow6, 7);
            drow7 = vld1_lane_u8(pSrc2, drow7, 7);
    
            dtmp0 = vshr_n_u8(drow0, 1);
            dtmp1 = vshr_n_u8(drow1, 1);
            dtmp2 = vshr_n_u8(drow2, 1);
            dtmp3 = vshr_n_u8(drow3, 1);
            dtmp4 = vshr_n_u8(drow4, 1);
            dtmp5 = vshr_n_u8(drow5, 1);
            dtmp6 = vshr_n_u8(drow6, 1);
            dtmp7 = vshr_n_u8(drow7, 1);
    
            qrow0 = vcombine_u8(drow0, dtmp0);
            qrow1 = vcombine_u8(drow1, dtmp1);
            qrow2 = vcombine_u8(drow2, dtmp2);
            qrow3 = vcombine_u8(drow3, dtmp3);
            qrow4 = vcombine_u8(drow4, dtmp4);
            qrow5 = vcombine_u8(drow5, dtmp5);
            qrow6 = vcombine_u8(drow6, dtmp6);
            qrow7 = vcombine_u8(drow7, dtmp7);
    
    //////////////////////////////////////
    
            qtmp0 = qrow0;
            qtmp1 = qrow1;
            qtmp2 = qrow2;
            qtmp3 = qrow3;
            qtmp4 = qrow4;
            qtmp5 = qrow5;
            qtmp6 = qrow6;
            qtmp7 = qrow7;
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp1, 1);
            qtmp2 = vsliq_n_u8(qtmp2, qtmp3, 1);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp5, 1);
            qtmp6 = vsliq_n_u8(qtmp6, qtmp7, 1);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp2, 2);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp6, 2);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp4, 4);
    
            vst1q_u8((uint8_t *)pDst, qtmp0); pDst += 2;
    
    //////////////////////////////////////
    
            qtmp0 = vshrq_n_u8(qrow0, 2);
            qtmp1 = vshrq_n_u8(qrow1, 2);
            qtmp2 = vshrq_n_u8(qrow2, 2);
            qtmp3 = vshrq_n_u8(qrow3, 2);
            qtmp4 = vshrq_n_u8(qrow4, 2);
            qtmp5 = vshrq_n_u8(qrow5, 2);
            qtmp6 = vshrq_n_u8(qrow6, 2);
            qtmp7 = vshrq_n_u8(qrow7, 2);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp1, 1);
            qtmp2 = vsliq_n_u8(qtmp2, qtmp3, 1);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp5, 1);
            qtmp6 = vsliq_n_u8(qtmp6, qtmp7, 1);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp2, 2);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp6, 2);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp4, 4);
    
            vst1q_u8((uint8_t *)pDst, qtmp0); pDst += 2;
    
            //////////////////////////////////////
    
            qtmp0 = vshrq_n_u8(qrow0, 4);
            qtmp1 = vshrq_n_u8(qrow1, 4);
            qtmp2 = vshrq_n_u8(qrow2, 4);
            qtmp3 = vshrq_n_u8(qrow3, 4);
            qtmp4 = vshrq_n_u8(qrow4, 4);
            qtmp5 = vshrq_n_u8(qrow5, 4);
            qtmp6 = vshrq_n_u8(qrow6, 4);
            qtmp7 = vshrq_n_u8(qrow7, 4);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp1, 1);
            qtmp2 = vsliq_n_u8(qtmp2, qtmp3, 1);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp5, 1);
            qtmp6 = vsliq_n_u8(qtmp6, qtmp7, 1);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp2, 2);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp6, 2);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp4, 4);
    
            vst1q_u8((uint8_t *)pDst, qtmp0); pDst += 2;
    
            //////////////////////////////////////
    
            qtmp0 = vshrq_n_u8(qrow0, 6);
            qtmp1 = vshrq_n_u8(qrow1, 6);
            qtmp2 = vshrq_n_u8(qrow2, 6);
            qtmp3 = vshrq_n_u8(qrow3, 6);
            qtmp4 = vshrq_n_u8(qrow4, 6);
            qtmp5 = vshrq_n_u8(qrow5, 6);
            qtmp6 = vshrq_n_u8(qrow6, 6);
            qtmp7 = vshrq_n_u8(qrow7, 6);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp1, 1);
            qtmp2 = vsliq_n_u8(qtmp2, qtmp3, 1);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp5, 1);
            qtmp6 = vsliq_n_u8(qtmp6, qtmp7, 1);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp2, 2);
            qtmp4 = vsliq_n_u8(qtmp4, qtmp6, 2);
    
            qtmp0 = vsliq_n_u8(qtmp0, qtmp4, 4);
    
            vst1q_u8((uint8_t *)pDst, qtmp0); pDst += 2;
    
        } while (--count);
    }

字符串
我尽了最大努力让编译器生成优化的机器码，但他们根本不听：godbolt特别是GCC（一如既往）。
我明天会添加一个汇编版本。

赞(0）回复(0）举报 11个月前

我来回答

assembly 基于SIMD的快速位矩阵(64x64)转置算法

4条答案

相关问题

热门标签

最新问答