SAFARI Research Group at ETH Zurich and Carnegie Mellon University's repositories
ramulator-pim
A fast and flexible simulation infrastructure for exploring general-purpose processing-in-memory (PIM) architectures. Ramulator-PIM combines a widely-used simulator for out-of-order and in-order processors (ZSim) with Ramulator, a DRAM simulator with memory models for DDRx, LPDDRx, GDDRx, WIOx, HBMx, and HMCx. Ramulator is described in the IEEE CAL 2015 paper by Kim et al. at https://people.inf.ethz.ch/omutlu/pub/ramulator_dram_simulator-ieee-cal15.pdf Ramulator-PIM is used in the DAC 2019 paper by Singh et al. at https://people.inf.ethz.ch/omutlu/pub/NAPEL-near-memory-computing-performance-prediction-via-ML_dac19.pdf
prim-benchmarks
PrIM (Processing-In-Memory benchmarks) is the first benchmark suite for a real-world processing-in-memory (PIM) architecture. PrIM is developed to evaluate, analyze, and characterize the first publicly-available real-world PIM architecture, the UPMEM PIM architecture. Described by Gómez-Luna et al. (https://arxiv.org/abs/2105.03814).
SparseP
SparseP is the first open-source Sparse Matrix Vector Multiplication (SpMV) software package for real-world Processing-In-Memory (PIM) architectures. SparseP is developed to evaluate and characterize the first publicly-available real-world PIM architecture, the UPMEM PIM architecture. Described by C. Giannoula et al. [https://arxiv.org/abs/2201.05072]
SneakySnake
SneakySnake:snake: is the first and the only pre-alignment filtering algorithm that works efficiently and fast on modern CPU, FPGA, and GPU architectures. It greatly (by more than two orders of magnitude) expedites sequence alignment calculation for both short and long reads. Described in the Bioinformatics (2020) by Alser et al. https://arxiv.org/abs/1910.09020.
BLEND
BLEND is a mechanism that can efficiently find fuzzy seed matches between sequences to significantly improve the performance and accuracy while reducing the memory space usage of two important applications: 1) finding overlapping reads and 2) read mapping. Described by Firtina et al. (published in NARGAB https://doi.org/10.1093/nargab/lqad004)
GenASM
Source code for the software implementations of the GenASM algorithms proposed in our MICRO 2020 paper: Senol Cali et. al., "GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis" at https://people.inf.ethz.ch/omutlu/pub/GenASM-approximate-string-matching-framework-for-genome-analysis_micro20.pdf
Sibyl
Source code for the software implementation of Sibyl proposed in our ISCA 2022 paper: Gagandeep Singh et. al., "Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems using Online Reinforcement Learning" at https://people.inf.ethz.ch/omutlu/pub/Sibyl_RL-based-data-placement-in-hybrid-storage-systems_isca22.pdf
AirLift
AirLift is a tool that updates mapped reads from one reference genome to another. Unlike existing tools, It accounts for regions not shared between the two reference genomes and enables remapping across all parts of the references. Described by Kim et al. (preliminary version at http://arxiv.org/abs/1912.08735)
BlockHammer
Source code for the cycle-level simulator and RTL implementation of BlockHammer proposed in our HPCA 2021 paper: Yaglikci et. al., "BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows" at https://people.inf.ethz.ch/omutlu/pub/BlockHammer_preventing-DRAM-rowhammer-at-low-cost_hpca21.pdf
NATSA
NATSA is the first near-data-processing accelerator for time series analysis based on the Matrix Profile (SCRIMP) algorithm. NATSA exploits modern 3D-stacked High Bandwidth Memory (HBM) to enable efficient and fast matrix profile computation near memory. Described in ICCD 2020 by Fernandez et al. https://people.inf.ethz.ch/omutlu/pub/NATSA_time-series-analysis-near-data_iccd20.pdf
GenStore
GenStore is the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. Described in the ASPLOS 2022 paper by Mansouri Ghiasi et al. at https://people.inf.ethz.ch/omutlu/pub/GenStore_asplos22-arxiv.pdf
SeGraM
Source code for the software implementation of SeGraM proposed in our ISCA 2022 paper: Senol Cali et. al., "SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping" at https://people.inf.ethz.ch/omutlu/pub/SeGraM_genomic-sequence-mapping-universal-accelerator_isca22.pdf
Pythia-HDL
Implementation of Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement Learning in Chisel HDL. To know more, please read the paper that appeared in MICRO 2021 by Bera et al. (https://arxiv.org/pdf/2109.12021.pdf).
COVIDHunter
COVIDHunter 🦠:construction:: An accurate and flexible COVID-19 outbreak simulation model that forecasts the strength of future mitigation measures and the numbers of cases, hospitalizations, and deaths for a given day, while considering the potential effect of environmental conditions. Described by Alser et al. (preliminary version at https://arxiv.org/abs/2102.03667 and https://doi.org/10.1101/2021.02.06.21251265).
ApHMM-GPU
ApHMM-GPU is the first GPU implementation of the Baum-Welch algorithm for profile Hidden Markov Models (pHMMs). It includes many of the software optimizations as proposed in the ApHMM paper, which is described by Firtina et al. (preliminary version at https://arxiv.org/abs/2207.09765).
MetaSys
Metasys is the first open-source FPGA-based infrastructure with a prototype in a RISC-V core, to enable the rapid implementation and evaluation of a wide range of cross-layer software/hardware cooperative techniques techniques in real hardware. Described in our ACM TACO paper: https://dl.acm.org/doi/full/10.1145/3505250
Molecules2Variations
The first work to provide a comprehensive survey of a prominent set of algorithmic improvement and hardware acceleration efforts for the entire genome analysis pipeline used for the three most prominent sequencing data, short reads (Illumina), ultra-long reads (ONT), and accurate long reads (HiFi). Described in arXiv (2022) by Alser et al. https://arxiv.org/abs/2205.07957
transpimlib
TransPimLib is a library for transcendental (and other hard-to-calculate) functions in general-purpose PIM systems, TransPimLib provides CORDIC-based and LUT-based methods for trigonometric functions, hyperbolic functions, exponentiation, logarithm, square root, etc. Described in ISPASS'23 paper by Item et al. (https://arxiv.org/pdf/2304.01951.pdf)
DRAM-Datasheet-Survey
A survey of manufacturer-provided DRAM operating parameters and timings as specified by DRAM chip datasheets from between 1970 and 2021. This data and its analysis are described in the 2022 paper by Patel et al.: https://arxiv.org/abs/2204.10378
TargetCall
TargetCall is the first pre-basecalling filter that is applicable to a wide range of use cases to eliminate wasted computation in basecalling. Described in our preprint: https://arxiv.org/abs/2212.04953
BioDynaMo
BioDynamo is a flexible and high-performance agent based simulation engine. This repository contains artifacts and materials to support the reproducibility of the paper: Breitwieser et al., "High-Performance and Scalable Agent-Based Simulation with BioDynaMo," accepted to PPoPP '23: https://arxiv.org/pdf/2301.06984.pdf
SNP-Selective-Hiding
An optimization-based mechanism :dna: :closed_lock_with_key: to selectively hide the minimum number of overlapping SNPs among the family members :family_man_woman_girl_boy: who participated in the genomic studies (i.e. GWAS). Our goal is to distort the dependencies among the family members in the original database for achieving better privacy without significantly degrading the data utility.
alignment-in-memory
AIM (Alignment-in-Memory), A Framework for High-throughput Sequence Alignment using Real Processing-in-Memory Systems, Bioinformatics, btad155, https://doi.org/10.1093/bioinformatics/btad155