Ragnar {Groot Koerkamp}

@curiouscoding.nl

1042 Followers 132 Following

Postdoc on high troughput bioinformatics @ KIT Karlsruhe; IMO, ICPC, Xoogler, Rust, road-cycling, hiking, wild camping, photography

Table of space lowerbounds for various bucket sizes k and load factors alpha.
Evo, a kid, wields the rolled-up Barbell poster in a fight against Andre with the metagraph poster rolled and folded up.
Nathaniel standing in front of his title slide at the start of the presentation.
DB app screenshot: train from Kilchberg (Zurich) to Copenhagen, leaving 4:25 and arriving 20:16.
A screenshot of Rust code: const BIST: u32 = 31; assert!(pos < (1 << BITS) as usize);
Reddit add for r/castiron writing "One pan to rule them all"
Figure showing a 2D edit-graph with diagonal segments showing anchors, and red diagonal segments showing an optimal chain of anchors. A coloured background of overlapping triangular-shaped regions with a horizontal top and diagonal bottom, with one colour per anchor, show the best anchor to use in each position.
Navarro'01: Ukkonen (1983). In 1983, Ukkonen [1985a] presented an algorithm able to compute the edit distance between two strings x and y in O(ed(x, y)^2) time, or to check in time O(k^2) whether that distance was ≤k or not. This is the first member of what has been called “diagonal transition algorithms,” since it is based on the fact that the diagonals of the dynamic programming matrix (running from the upper-left to the lower-right cells) are monotonically increasing (more than that, C[i+1, j+1] ∈{C[i, j], C[i, j +1]}). The algorithm is based on computing in constant time the positions where the values along the diagonals are incremented. Only O(k^2) such positions are computed to reach the lower-right decisive cell.
Plots with the density of the sus-anchor for sigma in 2, 4, 32 as a function of w. For sigma=4, they have an overhead over the lowerbound of <1%, while other schemes have >15% overhead.
Screenshot of ChatGTP answering a question about the 'diagonal transition' algorithm, with a low-contrast bubble saying 'CuriousCoding' and +1.
Example of a barbell command showing the flanks and barcodes being searched.
Sassy's grep-like output, with matches in green, and indels coloured red/blue.
Log-log time-per-operation plots for different workloads. The x-ax has increasing input sizes, and the y-ax has the normalized time per push-pop pair of operations divided by lg n. The binary heap, d-ary heap, and weak heap all slow down as n increases, while the I/O-efficient heaps all get faster. The SimdQuickHeap is 2x faster than the radix heap and up to 10x faster than the binary heap.
Overview of the SimdQuickHeap data structure: a list (vector) of pivots is stored, and the elements between consecutive pivots are stored in their own bucket (also a vector). The elements in a bucket are in between the two pivots surrounding it.
crates.io screenshot showin 6 crates, most of which *-rs, all created 2 days ago
crates.io screenshot showing 7 *-rs crates created in the last 2 days, like blast-rs, hmmer-pure-rs, and minimap2-pure-rs.
Screenshot showing the initialization of a perf counter that counts last-level cache misses. It then starts the counter, executes the function to be benchmarked, stops the counter, and reads its value.
doi: DOI HERE
Plot of minimizer density for DNA alphabet for w=12 and increasing k. The new "spacers" perform well for k=5..7 in this case.
Fig. 1: SIMD pattern tiling overview. First, the full patterns and their suffixes of length w′ bp are encoded using a profile (e.g DNA or IUPAC). Then, the text characters are processed one by one (left to right) and each character is compared against L′ pattern suffixes of length w′ bp using a w′ × L′ SIMD tiling (blue vertical bar, Section 2.2, shown for 4 × 4). The end positions of suffixes that match with ≤k errors are highlighted in yellow. When such positions are adjacent they form a single range R. We then verify whether the full pattern matches with ≤k errors at those end positions. The slices corresponding to each suffix range (2 ranges here), each of size |R| + m + k −1, are then in parallel verified against the full pattern using a w × L tiling (here 2 × 8). The local minima (single opaque cell) with value ≤k are selected for traceback (small black arrows) to report the matching text locations and CIGAR string.
Fig. 1: The tiling strategy used by Sassy. The text is first split into word-size blocks of 64 bases. Then, the list of blocks is split into 4 chunks that are processed in parallel, with one SIMD lane per chunk. The text is implicitly padded as needed. Within a chunk, filling the matrix proceeds block-by-block. For each block, all (up to, see Section 2.3 and Figure 3) m = |P | rows are computed before proceeding to the next block. Each chunk is extended into the succeeding chunk as long as there is a sufficiently good “in progress” alignment (not shown).
A plot, with caption: Fig. 5: Throughput of searching texts of varying length. We search a pattern of length m = 100 against texts with length varying from n = 150 to n = 128 000 bp, with k ∈{3, 20}. All points are computed by averaging over 1000 random texts and then converting to throughput. Note that this does not include searching the reverse-complement strand. While Sassy is consistently faster than Edlib, its relative advantage is smaller for shorter texts. The plot shows that Sassy is consistently ~4-8x faster than Edlib, and that both are relatively slow for short sequences and converge only for sequences of length >8kbp or so.
start-history.com graph of A*PA github stars, with bumps at time of preprint and publication, and steady growth since then.
A DFA of a variation graph, a tree representation of it, and the corresponding minimal Wheeler-DFA.
Schematic overview of the QuadRank data layout.
We see that AT and GC have strong local correlation in density in the human genome.
A plot like before, but now all lines are different. In particular, the orange line for C is nearly flat in the last quarter?!?!
Plot of the cumulative character count for each of ACTG in a human genome. 2 exactly straight lines are visible, with A and T overlapping, and C and G overlapping. They are at different slopes because they have different frequencies.
The recent KKMLT lower bound [7]
Picture of my laptop showing the startup menu, with next to it a set of tiny screwdrivers and a 32GB SO-DIMM ramstick.
Opening sentence of my rank paper, stating that rank queries are right exclusive.
Screenshot of quote tweet claiming 'you're probably just a Microsoft-paid troll'
Table comparing a random 3.2Gbp string against a human genome, HPRCv1, and HPRCv2. HPRCv1 has 94 copies and an estimated 33M mutations, while HPRCv2 has 466 copies and an estimated 68M mutations in total.
Screenshot from https://curiouscoding.nl/wip/#cfp-deadlines, showing paper-submission deadlines for upcoming conferences.
Photo of a shoerack with in it a nuc server and a HDD.
Screenshot of movi 2 section on PML. > PMLs relax the requirement that PML[i] equals the maximal prefix of suffix P[i..m] that matches T; rather, PMLs are usually proportional to but less than the MSs. In return for the approximate answer, the algorithm for computing PMLs is faster than those for computing MSs, as shown by Ahmed et al. (2021).
Screenshot of a space-time tradeoff figure from the movi2 paper. Ropebwt is much much slower than movi2, although it has comparable size.
Dec 15 00:00
Screenshot of some assembly code, showing 4x movq, then some loads from memory, then again 4x movq undoing the initial set of moves.
Screenshot of colourized alignment output of 20 500bp reads. Clearly visible are some columns where most reads have a blue A or G inserted with respect to the 'reference' read, and (on the right) where a T is substituted for a yellow G.
gif of output of running `sassy grep` in the terminal, showing the approximate matches of a 23bp pattern in a human genome, with up to k=2, 3, or 4 errors. Colours indicate the difference between the pattern and the match: match (green), substitution (orange), deletion (red), and insertion (blue).
Screenshot of a citation stating "Koerkamp GR", rather than "Groot Koerkamp, R".
Inside of the Andrew Wiles building
First time I have my own room! Translation from German: scientific employee, Dr. Ragnar Groot Koerkamp 😍
Screenshot showing `time` output of `gzip` and `deacon filter`. Unzipping takes 27s. Deacon takes only 11s with an empty index file (because it uses a faster gunzip implementation.) With `--mim`, which uses a .fastq.gz.mim index file, it goes down to 5.5s. When actually filtering the input against human genome minimizers, .fastq.gz input takes 15s, whereas the mim version takes 11s.
Picture of the contest hall (transformed mensa) with many tables in a grid-like setting. Each table has a laptop and 3 chairs, and notebooks and an envelope with problem statements.
Screenshot of the Cloudflare 'verifying you are human' page, with a spinner that just stays there forever.
screenshow of build logs on circleci, where at most 20% of the screen is actually showing text. The rest is margins, padding, headers, sidebar, and a lack of reflowing the text on resizing the window.
Screenshot of running `cargo-dist init`, which asks which platforms I want to release binaries for.
Screenshot from the rust survey showing two checked items: - Rust makes me more productive - Rust is fun to use
Slack's 'more unread messages' notifaction hiding the 'threads' button underneath it, where the new messages actually are.
A screenshot showing that I currently use 9 unstable features
for each query lookahead (=32) iterations ahead, prefetch, and then process the current one.