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RsDict: Fast rank/select over bitmaps

Rank and select are two useful operations on bitmaps for building more sophisticated data structures. First, the rank at a given index i counts the number of set bits left of i. For example, a sparse array can be represented as a dense array of the values present and a bitmap indicating which indices are present. Then, rank provides a function from an index into the sparse array to an index into the dense one.

Select is the inverse of rank, where select(B, k) returns the index of the kth set bit. To make the two inverses, we use zero-indexing for select (so select(B, 0) returns the index of the first bit set in B) and rank only counts indices strictly to the left of i. From our previous example, select allows going from an index in the dense array to the original sparse array.

This data structure implements these two operations efficiently on top of an append-only bitmap. It's an implementation of Navarro and Providel, "Fast, Small, Simple Rank/Select On Bitmaps", with heavy inspiration from a Go implementation. The underlying bitmap is stored in compressed form, so long runs of zeros and ones do not take up much space. The indices for rank and select add about 28% overhead over the uncompressed bitmap.

For more examples on how to use rank and select to build succinct datastructures, see Navarro's book on Compact Data Structures for an overview.

Implementation notes

This library is mostly a port of the Go implementation with a few additional optimizations.

SSE acceleration for rank

With the nightly-only simd feature and a CPU with SSSE3 support, the final step of rank is computed in a few steps without any loops. Turning this feature on improves the rsdict::rank benchmark by about 40% on my computer. See rank_acceleration.rs for more details.

Optimized routines for rank and select within a u64

With a CPU that supports popcnt, computing rank within a small block of 64 bits will use this instruction to efficiently count the number of bits set. Select uses an adapted version of an an algorithm from Daniel Lemire's blog that uses tzcnt to quickly skip over runs of trailing zeros.

Compact binomial coefficient lookup table

Encoding and decoding blocks of the compressed bitmap requires computing the binomial coefficient B(n, k) where 0 <= k <= n <= 64. Computing this on-the-fly is too expensive, so we store a precomputed lookup table of the coefficients. However, we exploit the symmetry of B in k to store only half the values. See build.rs for more details.

Performance

Here's some results from running the benchmark on my 2018 MacBook Pro with -C target-cpu=native.

rsdict::rank            time:   [10.330 us 10.488 us 10.678 us]
Found 4 outliers among 100 measurements (4.00%)
  4 (4.00%) high mild

jacobson::rank          time:   [17.958 us 18.335 us 18.740 us]
Found 6 outliers among 100 measurements (6.00%)
  1 (1.00%) high mild
  5 (5.00%) high severe

rank9::rank             time:   [6.8907 us 7.0768 us 7.2940 us]
Found 1 outliers among 100 measurements (1.00%)
  1 (1.00%) high severe

rsdict::select0         time:   [37.124 us 37.505 us 37.991 us]
Found 3 outliers among 100 measurements (3.00%)
  3 (3.00%) high severe

rsdict::select1         time:   [29.782 us 29.918 us 30.067 us]
Found 7 outliers among 100 measurements (7.00%)
  5 (5.00%) high mild
  2 (2.00%) high severe

rank9::binsearch::select0
                        time:   [229.64 us 231.54 us 233.87 us]
Found 5 outliers among 100 measurements (5.00%)
  2 (2.00%) high mild
  3 (3.00%) high severe

rank9::binsearch::select1
                        time:   [253.69 us 255.84 us 258.19 us]
Found 9 outliers among 100 measurements (9.00%)
  4 (4.00%) high mild
  5 (5.00%) high severe

So for rank queries, this implementation is faster than succinct-rs's Jacobson and slightly slower than its Rank9. However for select queries, it's much faster than doing binary search over these rank structures, so consider using this library if select is an important operation for your algorithm.

Testing

We use QuickCheck for testing data structure invariants. In addition, there's basic AFL fuzz integration to find interesting test cases using program coverage. Install cargo-afl and run the rsdict_fuzz binary with the fuzz feature set.

$ cargo install afl
$ cargo afl build --release --test rsdict_fuzz --features fuzz

# Create some starting bitsets within target/fuzz/in and create an empty directory target/fuzz/out.
$ cargo afl fuzz -i target/fuzz/in -o target/fuzz/out target/release/rsdict_fuzz