Tommaso Tarchi
TommasoTarchi
Software developer @ Istituto Nazionale di Astrofisica
Languages
Repos
11
Stars
4
Forks
0
Top Language
Python
Loading contributions...
Top Repositories
Two HPC exercises: 1. Parallel implementation of Conway's Game of Life using MPI and OpenMP concurrently; 2. Scalability study on libraries for basic linear algebra operations.
Automated pipeline for RNAseq data analysis using nextflow and singularity.
A simple, safe, easy to use CPU/GPU memory manager to work with OpenACC.
Study on the loss of data variability in (generative) variational autoencoders, with a focus on architectural modifications to mitigate the effect.
New implementation of a well-known stochastic model for carcinogenesis, augmented with competition dynamics and spatial structuring.
Numerical study on the phenomenon of sympatric speciation in both sexual and asexual species, within a static habitat with intra-species competition.
Repositories
11A simple, safe, easy to use CPU/GPU memory manager to work with OpenACC.
Study on the loss of data variability in (generative) variational autoencoders, with a focus on architectural modifications to mitigate the effect.
New implementation of a well-known stochastic model for carcinogenesis, augmented with competition dynamics and spatial structuring.
Numerical study on the phenomenon of sympatric speciation in both sexual and asexual species, within a static habitat with intra-species competition.
Automated pipeline for RNAseq data analysis using nextflow and singularity.
A stupid, useless, wrong math library project to experiment with software development methods.
Two HPC exercises: 1. Parallel implementation of Conway's Game of Life using MPI and OpenMP concurrently; 2. Scalability study on libraries for basic linear algebra operations.
My personal implementation of several unsupervised learning algorithms.
Jacobi method for Laplace equation and linear algebra exercises, using CUDA, OpenACC and MPI Remote Memory Access.
Implementation of a model for multi-objective sustainable waste collection optimization, using epsilon-constraint method and MOSA-MOIWOA.
Comparison of multiple machine learning algorithms for leaf classification.