SoR'23

The project plans to understand the impact of missing settings on performance of the target open-source database systems.

​ Hola Amigos! ​ As we draw the curtains on our project titled Public Artifact and Data Visualization we’re thrilled to present the incredible advancements we’ve achieved since our mid-term update.

Introduction For the second half of the project, I spent some time reproducing the figures and then focused on hacking the source code of PyTorch to distinguish the Inter-GPU Computation (1GPU vs.

Hi Everyone, I hope everything goes well! This is my second blog post for my project Reproducible Evaluation of Multi-level Erasure Coding under the mentorship of John Bent, Anjus George, and Meng Wang.

Introduction As part of the Reproducible Analysis & Models for Predicting Genomics Workflow Execution Time, our goal was to characterize the tools on genomic workflows in terms of system metrics and data quality to build machine learning models to predict the elapsed time of genomic workflows.

Hello everyone, This is my second blog post for SoR 2023. As you may recall from my initial blogpost, I am working on the Flashnet project under the mentorship of Haryadi S.

Mid-Term Report As part of the FlashNet my proposal under the mentorship of Haryadi S. Gunawi and Daniar Kurniawan aims to implement and optimize the FlashNet model in real-world storage systems using continual learning techniques.

The project plans to understand the impact of missing settings on performance of the target open-source database systems.

Over the past few weeks, our platform development has been progressing steadily, and we are excited to share the milestones we have achieved so far. As planned in our introductory blog, we have successfully laid the groundwork for the platform with the guidance and support of our mentor.

Greetings everyone, I’m Kangrui. Over the past few weeks, we’ve dedicated our efforts and have consequently made significant progress in our drift detection methods. Now, I’m excited to present to you a detailed elaboration on how we prompted and fine-tuned Llama2 to efficiently carry out the drift detection task.