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@jishnub jishnub commented Apr 10, 2025

This reduces TTFX.

julia> using LinearAlgebra

julia> T = Bidiagonal(ones(4), ones(3), :L);

julia> @time Matrix(T);
  0.073467 seconds (125.49 k allocations: 6.207 MiB, 99.83% compilation time) # nightly
  0.042144 seconds (46.61 k allocations: 2.310 MiB, 99.65% compilation time) # this PR
julia> T = Tridiagonal(ones(3), ones(4), ones(3));

julia> @time Matrix(T);
  0.077513 seconds (138.71 k allocations: 6.792 MiB, 99.85% compilation time) # master
  0.034220 seconds (37.27 k allocations: 1.867 MiB, 99.58% compilation time) # this PR

Performance is identical, as it's entirely dominated by setting the matrix to zero.

@jishnub jishnub added arrays [a, r, r, a, y, s] ttfx The change pertains to first-call latency labels Apr 10, 2025
@jishnub jishnub changed the title Explicit loop in converting Bidiagonal to Matrix Explicit loop in converting Bidiagonal/Tridiagonal to Matrix Apr 10, 2025
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codecov bot commented Apr 10, 2025

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 91.99%. Comparing base (a8fd121) to head (5576178).
Report is 2 commits behind head on master.

Additional details and impacted files
@@            Coverage Diff             @@
##           master    #1283      +/-   ##
==========================================
+ Coverage   90.19%   91.99%   +1.80%     
==========================================
  Files          34       34              
  Lines       15797    15503     -294     
==========================================
+ Hits        14248    14262      +14     
+ Misses       1549     1241     -308     

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@jishnub jishnub merged commit 2d27d1c into master Apr 11, 2025
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@jishnub jishnub deleted the jishnub/bidiag_matrix branch April 11, 2025 03:31
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