Space — the series
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Measuring what an algorithm keeps
Four counters measure what an algorithm does and none of them measures what it holds. An in-place sort and an out-of-place one with identical comparison counts are different algorithms, and until this phase the site had no way to say so. Two primitives close the gap, and the second of them counts something no array counter can ever see.
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The stack nobody counts
Merge sort makes 8,192 calls to sort 4,096 elements and holds fourteen of them at once. Depth-first search on a grid holds twelve vertices, or sixty-six, or a hundred and forty-four, depending on which of three equally standard implementations is running. The stack is a resource, it is the one that fails hard rather than slowly, and nothing that watches the data can see it.
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In place is a claim, and it is usually wrong about quicksort
Heapsort holds one slot at its peak. Quicksort holds twenty-two at n = 4,096 on random input and 4,097 on a sorted one. Merge sort holds 4,110. All three are described with the same two words, one of the three descriptions is false, and the false one is the algorithm the phrase is most often attached to.
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The frontier between time and space
The question of which sorting algorithm to use has an honest answer, and it is a shape rather than a name. Comparisons on one axis, peak auxiliary space on the other, and five of the ten algorithms here are on the Pareto frontier while five are dominated — beaten on both counts at once, so that no weighting of the two costs makes them the right choice. Heapsort is one of the five that lose.
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The space the model does not see
A slot is not a byte, a frame is not a slot, sixteen thousand allocations are not one allocation of the same size, and none of these numbers includes the input. The space counters are the newest instrument here and the honest account of what they miss is longer than the account of what they measure — including one bound this phase set out to demonstrate and could not.
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The tuples a summary does not report
A Greenwald–Khanna summary at ε = 0.01 answers `tuples` with seventy-seven. Watched through the run it holds a hundred and thirty-six. The gap is the compression period, it is 1.70 to 1.93 times across every tolerance measured, and it is the number a deployment has to allocate.
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The array that says where is twice the samples
An index keeps one suffix-array value in every thirty-two, and a bit vector over all n rows saying which. The vector is sixteen thousand bits and the values it points at are seven thousand — the index of the samples is twice the samples.
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A position split in two
Write each sorted position as a high part and a low part. Store the low parts packed and the high parts as a bit vector in which the k-th one sits at position (p >> w) + k. A select on that vector and a low read recover any position.
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The flat bottom of a shallow curve
The low width is chosen as the floor of log of the universe over the count. Rounding it up instead costs one bit on five thousand, because the total is m·w plus n over two to the w and the minimum is where those two are equal.
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Where the sparse representation loses
At every row marked, Elias–Fano costs twice the plain vector. The crossing is at one row in four, which is a sampling rate a real index uses — so the choice between the two is a choice, not an improvement.
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A price with no structure under it
A class in this collection has charged Elias–Fano's price for several strands and stores an array of positions searched by binary search. The accounting is right about space to a bit per thousand and wrong about one operation by a factor of eight.
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What the locating apparatus becomes
The two parts that answer "where" are half an index at a dense sampling and a fifth at a sparse one, and the fifth does not fall further. Represent the marks properly and it keeps falling, to under four per cent.
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The floor was the marks
A saving reported as about a sixth of a bidirectional index, falling to an eighth and levelling off. Represent one array properly and it falls to a fiftieth instead — most of what was being dropped was a badly encoded bit vector.
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Twenty bits apart
Two representations of one sparse set, six thousand seven hundred and forty-five bits against six thousand seven hundred and sixty-five. One exploits sparsity and the other exploits runs, and on this set at this density they price identically.
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Sparse on average, dense where it gathers
The r-index keeps one suffix-array value for every run of its transform and prices the set as Elias–Fano: on 32 exact copies of 2,048 characters that is 666 values among 65,537 positions at 9.04 bits a value, as though they were spread evenly. They are not — 663 sit in one copy. Cut into chunks of 32 values, each held by whichever of a bitmap, its own Elias–Fano or a run is cheapest, the same set costs 4.48 bits a value, under the 8.05 that any representation of an arbitrary set of that size must spend on some set. At a divergence of 0.0005 the saving is a fifth, and from one character in five hundred it is nothing but the select directory, which the chunks replace with reading. On the scattered marks of an ordinary index the partition saves only that directory too.
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Cuts that follow the gathering
Cutting an r-index's run-start values into chunks of a fixed count left chunk edges inside the places the values gather. Choosing the cuts as a shortest path through the sorted values was predicted to save a further tenth on exact copies by cutting once at each edge of the gathering. It cuts there, and saves more than a quarter: two chunks, one of them a bitmap of 664 values that a select must scan from its start. Capped at the chunk's old size, the optimum keeps the reads and saves 9% on exact copies and 15% at a divergence of 0.0005. On moderately drifted copies the uncapped optimum is smaller and cheap to read at once.
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A select that starts near its answer
The cheapest partition of an r-index's run-start values held exact copies in two chunks, one a bitmap of 664 values that every select scanned from its start: 1,065 bits read a select. Capping the chunks bought the reads back by giving up the saving. A select sample every 64 values inside each chunk, its bits charged in the same shortest path, was predicted to bring the uncapped optimum under 150 bits a select for under 3.5 bits a value. It reads 111 for 3.45, where the cap that reads as little costs 3.68. And the middle divergences, predicted to be untouched because their chunks were short, lose two fifths of their reads to the same samples.
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A sample that has to be found
A select sample every 64 values inside the cheapest partition of an r-index's run-start values cut a select's reads from 1,065 bits to 111 on exact copies. Samples by count bound how many values a select passes, not how many bits it reads, so samples every 128 bits of a chunk's vector were predicted to read a third less on the sparse, drifted sets at the same cost. They bound the scan as promised, but a rank sample does not say which sample a select needs, and finding it by binary search costs a select up to a third of what it reads. The two kinds of sample end up on one frontier, and the rank samples' advantage is that they come in finer steps.
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A second level that pays in one band
A select on a chunked bit vector can be helped two ways: a select sample every t values says where to start but not how far to scan, and a rank sample every s bits bounds the scan but must be searched for. A directory with both — select samples that name a rank block, rank samples that bound the scan — was predicted to reach under 70 bits read a select for under 3.6 bits a value on an r-index's run starts over exact copies, a point neither kind reaches. It reaches 62 bits read for 3.76 bits a value, which neither kind reaches, and costs 3.73 within 70 bits where the better single kind costs 3.93; but under 3.6 bits a value it reads 84. The select sample names a block two rank samples away, not one. Across eight sets the second level saves more than a twentieth of a bit a value on three, and only between about 65 and 100 bits read a select: outside that band either single kind is as good.