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#ethz

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EPFL SIC

📅 Join the EPFL Sustainable Innovation Challenge on March 13-14, 2025 at Rolex Learning Center, EPFL Lausanne! 🌍

🏆 12 student teams compete for CHF 30,000.
🔨 Expositions from startups, labs, and NGOs.
🔬 Keynotes and workshops with 14 speakers.
🗳️ Academic Citizens' Assembly on sustainability.
📈 Network with experts and industry leaders.

Register now & view the program using the Schedule & tickets link! epfl-sic.ch/

#sustainability#students#epfl
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:rss: Hacker News

Static search trees: faster than binary search
curiouscoding.nl/posts/static-
#ycombinator #blog #developer #phd #computational_biology #dna #sequencing #personal #bmi #eth #ethz #eth_zurich #pairwise_alignment

CuriousCoding · Static search trees: 40x faster than binary searchTable of Contents 1 Introduction 1.1 Problem statement 1.2 Motivation 1.3 Recommended reading 1.4 Binary search and Eytzinger layout 1.5 Hugepages 1.6 A note on benchmarking 1.7 Cache lines 1.8 S-trees and B-trees 2 Optimizing find 2.1 Linear 2.2 Auto-vectorization 2.3 Trailing zeros 2.4 Popcount 2.5 Manual SIMD 3 Optimizing the search 3.1 Batching 3.2 Prefetching 3.3 Pointer arithmetic 3.3.1 Up-front splat 3.3.2 Byte-based pointers 3.3.3 The final version 3.4 Skip prefetch 3.5 Interleave 4 Optimizing the tree layout 4.1 Left-tree 4.2 Memory layouts 4.3 Node size \(B=15\) 4.3.1 Data structure size 4.4 Summary 5 Prefix partitioning 5.1 Full layout 5.2 Compact subtrees 5.3 The best of both: compact first level 5.4 Overlapping trees 5.5 Human data 5.6 Prefix map 5.7 Summary 6 Multi-threaded comparison 7 Conclusion 7.1 Future work 7.1.1 Branchy search 7.1.2 Interpolation search 7.1.3 Packing data smaller 7.1.4 Returning indices in original data 7.1.5 Range queries 7.1.6 Sorting queries 7.1.7 Suffix array searching In this post, we will implement a static search tree (S+ tree) for high-throughput searching of sorted data, as introduced on Algorithmica. We’ll mostly take the code presented there as a starting point, and optimize it to its limits. For a large part, I’m simply taking the ‘future work’ ideas of that post and implementing them. And then there will be a bunch of looking at assembly code to shave off all the instructions we can. Lastly, there will be one big addition to optimize throughput: batching.
Public
:rss: Hacker News

Static search trees: 40x faster than binary search
curiouscoding.nl/posts/static-
#ycombinator #blog #developer #phd #computational_biology #dna #sequencing #personal #bmi #eth #ethz #eth_zurich #pairwise_alignment

CuriousCoding · Static search trees: 40x faster than binary searchTable of Contents 1 Introduction 1.1 Problem statement 1.2 Motivation 1.3 Recommended reading 1.4 Binary search and Eytzinger layout 1.5 Hugepages 1.6 A note on benchmarking 1.7 Cache lines 1.8 S-trees and B-trees 2 Optimizing find 2.1 Linear 2.2 Auto-vectorization 2.3 Trailing zeros 2.4 Popcount 2.5 Manual SIMD 3 Optimizing the search 3.1 Batching 3.2 Prefetching 3.3 Pointer arithmetic 3.3.1 Up-front splat 3.3.2 Byte-based pointers 3.3.3 The final version 3.4 Skip prefetch 3.5 Interleave 4 Optimizing the tree layout 4.1 Left-tree 4.2 Memory layouts 4.3 Node size \(B=15\) 4.3.1 Data structure size 4.4 Summary 5 Prefix partitioning 5.1 Full layout 5.2 Compact subtrees 5.3 The best of both: compact first level 5.4 Overlapping trees 5.5 Human data 5.6 Prefix map 5.7 Summary 6 Multi-threaded comparison 7 Conclusion 7.1 Future work 7.1.1 Branchy search 7.1.2 Interpolation search 7.1.3 Packing data smaller 7.1.4 Returning indices in original data 7.1.5 Range queries 7.1.6 Sorting queries 7.1.7 Suffix array searching In this post, we will implement a static search tree (S+ tree) for high-throughput searching of sorted data, as introduced on Algorithmica. We’ll mostly take the code presented there as a starting point, and optimize it to its limits. For a large part, I’m simply taking the ‘future work’ ideas of that post and implementing them. And then there will be a bunch of looking at assembly code to shave off all the instructions we can. Lastly, there will be one big addition to optimize throughput: batching.
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RTN

#SRF:
"
Starlab Space - Wird Dübendorf zur zukünftigen Weltraum-Hauptstadt?
"
".. Auf dem Gelände .. wird das Unternehmen unter anderem an der Nachfolge der internationalen Raumstation ISS forschen und dabei eng mit ETH und Universität Zürich zusammenarbeiten. .."

srf.ch/news/schweiz/starlab-sp

23.12.2024

Schweizer Radio und Fernsehen (SRF)Starlab Space: Dübendorf wird zentral für die WeltraumforschungDie Weltraumforschung in Dübendorf könnte zu einer Goldgrube werden, ist Elisabeth Stark von der Uni Zürich überzeugt.
#Dübendorf#ETHZ#Raumfahrt
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RTN

#ETHZ:
"
«Geoengineering löst das Problem des Klimawandels nicht»

Ein Team um ETH-Klimaforscher Sandro Vattioni hat gezeigt, dass sich Diamantenstaub in der Atmosphäre gut eignen könnte, um das Klima abzukühlen. Eine nachhaltige Lösung für den Klimawandel ist das aber trotzdem nicht, sagt Vattioni im Interview mit ETH News.
"
ethz.ch/de/news-und-veranstalt

24.10.2024

ETH Zürich«Geoengineering löst das Problem des Klimawandels nicht»
#Atmosphäre#Diamant#Erde
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DrYak

From our #ETHZ / @SIB research group Computational Biology Group (CBG):
Release of V-pipe 3.0

We're thrilled to announce the release of V-pipe 3.0! This computational pipeline is specifically crafted for analyzing short viral genomes using next-generation sequencing data. V-pipe 3.0 enables sustainable viral genomic data science. You can check out the published paper on our work in GigaScience at: doi.org/10.1093/gigascience/gi.

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