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Lecture 15 Loop Transformations. Chapter 11.10-11.11. Loop Optimization. Domain Loops: Change the order in which we iterate through loops Goals Minimize inner loop dependences that inhibit software pipelining Minimize loads and stores Parallelism - PowerPoint PPT Presentation
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Carnegie Mellon
Lecture 15Loop Transformations
Chapter 11.10-11.11
Dror E. Maydan CS243: Loop Optimization and Array Analysis 1
Carnegie Mellon
Loop Optimization
• Domain– Loops: Change the order in which we iterate through loops
• Goals– Minimize inner loop dependences that inhibit software pipelining– Minimize loads and stores– Parallelism
• SIMD Vector today, in general multiprocessor as well– Minimize cache misses– Minimize register spilling
• Tools– Loop interchange– Fusion– Fission– Outer loop unrolling– Cache Tiling– Vectorization
• Algorithm for putting it all together
Dror E. MaydanCS243: Loop Optimization and Array Analysis 2
Carnegie Mellon
Loop Interchange
for i = 1 to n for j = 1 to n
A[j][i] = A[j-1][i-1] * b[i]
• Should I interchange the two loops?– Stride-1 accesses are better for caches– But one more load in the inner loop– But one less register needed to hold the result of the loop
Dror E. MaydanCS243: Loop Optimization and Array Analysis 3
for j = 1 to n for i = 1 to n A[j][i] = A[j-1][i-1] * b[i]
Carnegie Mellon
Loop Interchange
for i = 1 to n for j = 1 to n A[j][i] = A[j+1][i-1] * b[i]
Distance Vector is (deltai, deltaj) = (1, -1)Direction Vector is (>, <)
• Dependence represents that one ref, aw must happen before another ar
• To permute loops, permute direction vectors in the same manner– Permutation is legal iff all permuted direction vectors are lexicographically positive
• Special case: Fully permutable loop nest– Either dependence “carried” by a loop outside of the nest or all components > or =
• All the loops in the nest can be arbitrarily permuted– (>, >, <) Inner two loops are fully permutable– (>=, =, >) All three loops are fully permutable
Dror E. MaydanCS243: Loop Optimization and Array Analysis 4
j
i
Carnegie Mellon
Loop Interchange
for i = 1 to n for j = 1 to i
…
• How do I interchangefor j = 1 to n for i = j to n
– In general ugly but doable
Dror E. MaydanCS243: Loop Optimization and Array Analysis 5
j
i
Carnegie Mellon
Non Perfectly Nested loops
for i = 1 to n for j = 1 to n
S1 for j = 1 to n
S2
• Can’t always interchange– Can be expensive when you can
Dror E. MaydanCS243: Loop Optimization and Array Analysis 6
Carnegie Mellon
Loop Fusion
for i = 1 to n for j = 1 to nS1 for j = 1 to nS2
• Moving S2 across “j” iterations but not any of “i” iterations• Pretend to fuse• Legal as long as there is no direction vector from S2 to S1 with “=“ in all
the outer loops and > in one of the inner (=, =, …, =, >, …),– That would imply that S2 is now before S1
Dror E. MaydanCS243: Loop Optimization and Array Analysis 7
for i = 1 to n
for j = 1 to n
S1
S2
Carnegie Mellon
Loop Fusion
for i = 1 to n for j = 1 to n
a[i][j] = … for j = 1 to n
… = a[i][j+1] • Legal as long as there is no direction vector from the read to the
write with “=“ in all the outer loops and > in one of the inner (=, =, …, =, >, …)– (=, 1) so can’t fusion
Dror E. MaydanCS243: Loop Optimization and Array Analysis 8
Carnegie Mellon
Loop Fusion
for i = 1 to n for j = 1 to n
a[i][j] = … for j = 1 to n
… = a[i][j+1]
If the first “+” direction is always a small literal constant, can skew the loop and allow fusion
Bonus: can get rid of a load and maybe a store
Dror E. MaydanCS243: Loop Optimization and Array Analysis 9
for i = 1 to n
a[i][1] = …
for j = 2 to n
a[i][j] = …
for j = 1, n-1
… = a[i][j+1]
… = a[i][n+1]
for i = 1 to n
a[i][1] = …
for j = 2 to n
a[i][j] = …
for j = 2, n
… = a[i][j]
… = a[i][n+1]
for i = 1 to n
a[i][1] = …
for j = 2 to n {
a[i][j] = …
… = a[i][j]
}
… = a[i][n-1]
Carnegie Mellon
Loop Fission
for i = 1 to n for j = 1 to n
S1 for j = 1 to n
S2
• Moving S2 across all later “i” iterations– Legal as long as no dependences from S2 to S1 with > in the
fissioned outer loops
Dror E. MaydanCS243: Loop Optimization and Array Analysis 10
for i = 1 to n
for j = 1 to n
S1
for i = 1 to n
for j = 1 to n
S2
Carnegie Mellon
Loop Fission
for i = 1 to n for j = 1 to n
= a[i-1][j] for j = 1 to n
a[i][j] =
• Moving S2 across all later “i” iterations– Legal as long as no dependences from S2 to S1 with > in the
fissioned outer loops
Dror E. MaydanCS243: Loop Optimization and Array Analysis 11
for i = 1 to n
for j = 1 to n
= a[i-1][j]
for i = 1 to n
for j = 1 to n
a[i][j] = Dep from write to read of (1)
Carnegie Mellon
Inner Loop Fission
for i = 1 to n for j = 1 to n… = h[i];… = h[i+1];… … = h[i+49];… = h[i+50];
• Legal as long as there is no dependence from an S2 to an S1 where the first “>” is in the “j” loop.
Dror E. MaydanCS243: Loop Optimization and Array Analysis 12
for i = 1 to n
for j = 1 to n
= h[i];
…
… = h[i+25];
for j = 1 to n
… = h[26];
…
… = h[50];
Carnegie Mellon
Inner Loop Fission
for j = 1 to nS1S2S3
• Looking at edges carried by the inner most loops• Strongly Connected Components can not be fissioned• Everything else can be fissoned as long as loops are emitted in topological order
Dror E. MaydanCS243: Loop Optimization and Array Analysis 13
S1
S2
S3
=
=>
Carnegie Mellon
Outer Loop Unrolling
for i = 1 to n for j = 1 to n
for k = 1 to n c[i][j] += a[i][k] * b[k][j];
• How many loads in the inner loop? How many MACs?
Dror E. MaydanCS243: Loop Optimization and Array Analysis 14
Carnegie Mellon
Outer Loop Unrolling
for i = 1 to n by 2 for j = 1 to n by 2for k = 1 to n c[i][j] += a[i][k] * b[k][j]; c[i][j+1] += a[i][k] * b[k][j+1]; c[i+1][j] += a[i+1][k] * b[k][j]; c[i+1][j+1] += a[i+1][k] * b[k][j+1];
• Is it legal?
Dror E. MaydanCS243: Loop Optimization and Array Analysis 15
Carnegie Mellon
Outer Loop Unrolling
If n = 2– Original order was
• (1, 1, 1) (1, 1, 2) (1, 2, 1) (1, 2, 2) (2, 1, 1) (2, 1, 2) (2, 2, 1) (2, 2, 2)
– New order is • (1, 1, 1) (1, 2, 1) (2, 1, 1) (2, 2, 1) (1, 1,
2) (1, 2, 2) (2, 1, 2) (2, 2, 2)• Equivalent to permuting the loops into
for k = 1 to 2 for i = 1 to 2 for j = 1 to 2
• If loops are fully permutable can also outer loop unroll
Dror E. MaydanCS243: Loop Optimization and Array Analysis 16
for i = 1 to 2 by 2
for j = 1 to 2 by 2
for k = 1 to 2
c[i][j] += a[i][k] * b[k];
c[i][j+1] += a[i][k] * b[k][j+1];
c[i+1][j] += a[i+1][k] * b[k][j];
c[i+1][j+1] += a[i+1][k] * b[k][j+1];
Carnegie Mellon
Unrolling Trapezoidal Loops
Dror E. MaydanCS243: Loop Optimization and Array Analysis 17
j
i
• Ugly• We unroll two level trapezoidal loops but the details are very ugly
for i=1 to n by 2 for j = 1 to i
Carnegie Mellon
Trapezoidal Example
for (i=0; i<n; i++) { for (j=2*i; j<n-i; j++) {
a[i][j] += 1; }
}
D.E. MaydanCS243: Loop Optimization and Array Analysis 18
for(i = 0; i <= (n + -2); i = i + 2) { lstar = (i * 2) + 2; ustar = (n - (i + 1)) + -1; if(((i * 2) + 2) < (n - (i + 1))) { for(r2d_i = i; r2d_i <= (i + 1); r2d_i = r2d_i + 1){ for(j = r2d_i * 2; j <= ((i * 2) + 1); j = j + 1){ a[r2d_i][j] = a[r2d_i][j] + 1; } } for(j0 = lstar; ustar >= j0; j0 = j0 + 1) { a[i][j0] = a[i][j0] + 1; a[i + 1][j0] = a[i + 1][j0] + 1; }; for(r2d_i0 = i; r2d_i0 <= (i + 1); r2d_i0 = r2d_i0 + 1) { for(j1 = n - (i + 1); j1 < (n - r2d_i0); j1 = j1 + 1) { a[r2d_i0][j1] = a[r2d_i0][j1] + 1; }; } } else { for(r2d_i1 = i; r2d_i1 <= (i + 1); r2d_i1 = r2d_i1 + 1) { for(j2 = r2d_i1 * 2; j2 < (n - r2d_i1); j2 = j2 + 1) { a[r2d_i1][j2] = a[r2d_i1][j2] + 1; } } } } if(n > i) { for(j3 = i * 2; j3 < (n - i); j3 = j3 + 1) { a[i][j3] = a[i][j3] + 1; }; }
Carnegie Mellon
Cache Tiling
for i = 1 to n for j = 1 to n
for k = 1 to n c[i][j] += a[i][k] * b[k][j];
• How many cache misses?
Dror E. MaydanCS243: Loop Optimization and Array Analysis 19
Carnegie Mellon
Cache Tiling
for jb = 1 to n by b for kb = 1 to n by b for i = 1 to nfor j = jb to jb+b for k = kb to kb + b c[i][j] += a[i][k] * b[k][j];
• How many cache misses?– Order b reuse for each array
• If loops are fully permutable can cache tile
Dror E. MaydanCS243: Loop Optimization and Array Analysis 20
Carnegie Mellon
Vectorization: SIMD
for i = 1 to nfor j = 1 to n a[j][i] = 0;
• N-way parallel where N is the SIMD width of the machine
Dror E. MaydanCS243: Loop Optimization and Array Analysis 21
for i = 1 to n by 8 for j = 1 to n a[j][i:i+7] = 0;
Carnegie Mellon
Vectorization: SIMDfor i = 1 to n
for j = 1 to n for k = 1 to n S1
….SM
• We have moved later iterations of S1 ahead of earlier iterations of S2, …, SM, etc
• Legal as long as no dependence from a latter S to an earlier S where that dependence is carried by the vector loop– E.g legal to vectorize ‘j’ above if no dependence from a latter S to an
earlier S with direction (=, >, *)
Dror E. MaydanCS243: Loop Optimization and Array Analysis 22
Carnegie Mellon
Putting It All Together
• Three phase algorithm1. Use fission and fusion to build perfectly nested loops
1. We prefer fusion but not obvious that that is right2. Enumerate possibilities for unrolling, interchanging, cache
tiling and vectorizing3. Use inner loop fission if necessary to minimize register
pressure
Dror E. MaydanCS243: Loop Optimization and Array Analysis 23
Carnegie Mellon
Phase 2
Choose a loop to vectorize All references that refer to vector loop must be stride-1For each possible inner loop
Compute best possible unrollings for each outerCompute best possible ordering and tiling
To compute best possible unrollingTry all combinations of unrolling up to a max product of 16For each possible unrolling
Estimate the machine cycles for the inner loop (ignoring cache)Estimate the register pressure
Don’t unroll more if too much register pressure
To compute best possible ordering and tilingConsider only loops with “reuse”Choose best threeIterate over all orderings of three with a binary search on cache tile sizeNote and record the total cycle time for this configuration. Pick the best
Estimating cyclesCould compile every combination, but …
Dror E. MaydanCS243: Loop Optimization and Array Analysis 24
Carnegie Mellon
Machine Modeling
• Recall that software pipelining had resource limits and latency limits– Map high level IR to machine resources– Unroll high level IR operations
• Remove duplicate loads and stores• Count machine resources• Build a latency graph of unrolled operations
– Iterate over inner loop cycles and find worst cycle• Assume performance is worst of two limits
• Model register pressure– Count loop invariant loads and stores– Count address streams– Count cross iteration cse’s = a[i] + a[i-2]– Add machine dependent constant
Dror E. MaydanCS243: Loop Optimization and Array Analysis 25
Carnegie Mellon
Cache Modeling
• Given a loop ordering and a set of tile factors• Combine array references that differ by constant, e.g. a[i][j] and
a[i+1][j+1]• Estimate capacity of all array references, multiply by fudge factor
for interference, stop increasing block sizes if capacity is larger than cache
• Estimate quantity of data that must be brought into cache
Dror E. MaydanCS243: Loop Optimization and Array Analysis 26
Carnegie Mellon
Phase 3: Inner loop fission
• Does inner loop use too many registers– Break down into SCCs– Pick biggest SCC
• Does it use too many registers– If yes, too bad– If no, search for other SCCs to merge in
» Pick one with most commonality » Keep merging while enough registers
Dror E. MaydanCS243: Loop Optimization and Array Analysis 27
Carnegie Mellon
Extra: Reductions
for i = 1 to n for j = 1 to n
a[j] += b[i][j];
Can I unroll
for i = 1 to n by 2 for j = 1 to n a[j] += b[i][j]; a[j] += b[i+1][j];• Legal
– Integer: yes– Floating point: maybe
Dror E. MaydanCS243: Loop Optimization and Array Analysis 28
Carnegie Mellon
Extra: Outer Loop Invariants
for i for j
a[i][j] += b[i] * cos(c[j])
Can replace with
for j t[j] = cos(c[j])for i for j
a[i][j] += b[i] * t[j];
• Need to integrate with model – Model must assume that invariant computation will be replaced with loads
Dror E. MaydanCS243: Loop Optimization and Array Analysis 29