![]() It also includes additional notes and future work proposals. The aforementioned notebook contains a more in-depth description and analysis of the proposed designs. The measurements were collected on different devices, namely an Intel 8-cores Xeon processor, an Nvidia Tesla T4 and an Nvidia RTX 2080 Ti.Īs expected, the GPU implementation is clearly superior in terms of performance, being as low as 3% of the CPU time and $8.2\times$ faster than CPU on average, with a peak of $27.2\times$. The tests have been conducted over precomputed grids of up to 5 million nodes. We believe that the main reason for such result lies in the fact that minres is able to best leverage the symmetry and the sparseness of the stiffness matrix compared to the other solver algorithms. Custom CUDA kernel implementation via NumbaĪs we can see in the following figure, the minres solver is the best performing one, especially in the case of large matrix dimensions.Batched CPU implementation via Numpy and Numba.In this work, we proposed four different implementations for computing the assembly of the stiffness matrix: The FEM algorithm is mainly divided in two phases: the assembly of stiffness matrix $K$ and the linear solver part. In the end, we show an evaluation of the three different proposed K-assembly steps, before drawing our conclusions. An additional K-assembly implementation based on cell coloring is proposed in the Appendix as a work in progress. Then, we proceed to evaluate different solver strategies and select the best performing solver algorithm.Īt this point, we present three different implementation of the K-assembly step in the FEM algorithm. Easily pull out URLs, airport codes, places, timespans etc from a string. Sort of like a simplified RegEx in Swift with features like conversions. We then report a simple mechanism to generate "large enough" FEM problems. Some great new String parsing features have been added to SoulverCore. In the first part of the notebook, we describe the FEM algorithm from a higher point of view. GitHub to introduce mandatory 2FA authentication starting March 13 - Help Net Security Skip to main content LinkedIn. On first launch the tool will automatically download a 1MB package file titled SoulverCoreSoulverCore.bundle to the same directory from which the tool is run. The Jupyter Notebook TRA105_GPU_accelerated_Computational_Methods_using_Python_and_CUDA.ipynb includes most of the project work and can be run in Google Colab. brew install soulver-cli The Soulver CLI is also available as a downloadable binary from the Github releases page for this repository (or by cloning this repository). The main contributions are given by Stefano Ribes (ribes dot stefano at gmail dot com), who developed all the high performance code, Kim Louisa Auth (kim dot auth at chalmers dot se), who wrote an initial version of the FEM algorithm, and Fredrik Larsson (Fredrik dot Larsson at chalmers dot se), who supervised the project. This repository includes the work done within the course TRA105 - GPU-accelerated Computational Methods using Python and CUDA, held at Chalmers University. See their respective subdirectories in this project for details.GPU-accelerated Finite Element Method using Python and CUDA Sliver is licensed under GPLv3, some sub-components may have separate licenses. Please take a moment and fill out our survey License - GPLv3 We also tend to hang out in the #golang Slack channel on the Bloodhound Gang server. Please checkout the wiki, or start a GitHub discussion. Linux One LinerĬurl |sudo bash and then run sliver Help! To get the very latest and greatest compile from source. Windows process migration, process injection, user token manipulation, etc.ĭownload the latest release and see the Sliver wiki for a quick tutorial on basic setup and usage.Fully scriptable using JavaScript/TypeScript or Python. ![]() Secure C2 over mTLS, WireGuard, HTTP(S), and DNS.GitHub - genedvlpr/math-solver: Solving mathematical equations using Artificial order now. Implants are supported on MacOS, Windows, and Linux (and possibly every Golang compiler target but we've not tested them all). Solving mathematical equations using Artificial Intelligence for Android. The server and client support MacOS, Windows, and Linux. Sliver's implants support C2 over Mutual TLS (mTLS), WireGuard, HTTP(S), and DNS and are dynamically compiled with per-binary asymmetric encryption keys. Sliver is an open source cross-platform adversary emulation/red team framework, it can be used by organizations of all sizes to perform security testing.
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