Parallel Computing and Optimization Techniques

GeneF is a PIM accelerator for DNA alignment that achieves significant speedups and energy efficiency over CPU implementations.

MolAR is a memory-safe Rust library for analyzing MD simulations, demonstrating high performance and offering memory-safe abstractions for molecular analysis software.

mm2-gb accelerates Minimap2's chaining step on GPU, improving performance for long DNA reads without sacrificing accuracy.

CUDASW++4.0 improves protein sequence database search speed using GPU-based Smith-Waterman alignment with performance gains over previous methods and CPU tools.

BIMSA accelerates long sequence alignment using Processing-In-Memory, achieving significant speedups over existing PIM and CPU implementations.

STORMM is a high-performance molecular simulation engine optimized for CPUs and GPUs, offering improved throughput and accuracy for small molecule and protein simulations.

DTWax optimizes the sDTW algorithm for GPU to improve sequencing speed and compute efficiency in nanopore sequencing.

This paper introduces a GPU-based framework that accelerates neuron simulations and applies it to neuroscience and AI tasks, showing significant speed improvements over traditional methods.

NeuroDevSim simulates neural development using shared memory parallelization, achieving near-linear scaling up to 128 cores for medium to large models.

NeuroGPU accelerates biophysically detailed neuronal simulations on GPUs, enabling faster parameter exploration and model fitting.

This paper accelerates FreeSurfer using OpenACC, reducing programming costs and improving performance with GPU support.

CLIJ provides GPU-accelerated image processing through a Fiji plugin, enabling faster workflows on consumer hardware.

This paper demonstrates that GPU-accelerated machine learning libraries significantly speed up and reduce the cost of computational genomics tasks like QTL mapping and Bayesian non-negative matrix factorization.