Proposals

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Current

A Framework for Systematic Performance Experiments for Machine Learning Analysis of Checkpointing during Deep Learning Model Training BLOX for Deep Learning Task Scheduling with GPU Collocation Data Attribution on Progressive Datasets for Deep Learning Efficient Data Selection Methods for Machine Learning Flexible Data Placement on SSDs for Database Systems How much Data Movement is Being Saved? Incremental Model Updates on Tiny Hardware Machine Learning Models for the Edge Resource Management on Tiny Hardware Exploiting Edge Devices for Data-Intensive Applications

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Previous

Efficient OS-level Context-Switching for Thread Migration Leveraging Heterogeneous Hardware Micro-architectural Analysis of SystemML What is HTAP? Workload Characterization for Big Data Management Analysis of New Generation SSDs Resource management for Data-Intensive Systems Running on Heterogeneous Hardware Unity DOTS Profiling Survey of hash-based authentication on modern hardware Analysis of NVMe SSDs and the IO stack Workload Characterization for Machine Learning Hardware Utilization Estimation with Machine Learning Data Preprocessing Pipelines Disk Access Tracing for Data-Intensive Systems Profiling Infrastructure for DAPHNE Studying Collocation for Machine Learning Managing Experimental Data with DuckDB Analysis of Predicting GPU Utilization Metrics Using Deep Learning Models and infering number of required SMs

Projects

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Current

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Previous

An Analysis of Open-Source HTAP Platforms: Towards a Standardized Benchmarking Suite Scalable Speech Recognition Training for Speech Recognition on Co-processors Application characteristics and scheduling in multi-tenant systems Comparing Intel's oneAPI to traditional multi-threaded CPP code HTAP benchmarking with an HTAP/IoT workload