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Towards a General Framework for ML-based Self-tuning Databases
Machine learning (ML) methods have recently emerged as an effective way ...
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MixBoost: A Heterogeneous Boosting Machine
Modern gradient boosting software frameworks, such as XGBoost and LightG...
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Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs
Solid-state drives (SSDs) are extensively used to deploy persistent data...
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Compiling Neural Networks for a Computational Memory Accelerator
Computational memory (CM) is a promising approach for accelerating infer...
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Safe and Efficient Remote Application Code Execution on Disaggregated NVM Storage with eBPF
With rapid improvements in NVM storage devices, the performance bottlene...
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SySCD: A System-Aware Parallel Coordinate Descent Algorithm
In this paper we propose a novel parallel stochastic coordinate descent ...
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Breadth-first, Depth-next Training of Random Forests
In this paper we analyze, evaluate, and improve the performance of train...
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Parallel training of linear models without compromising convergence
In this paper we analyze, evaluate, and improve the performance of train...
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Snap Machine Learning
We describe an efficient, scalable machine learning library that enables...
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Elevating commodity storage with the SALSA host translation layer
To satisfy increasing storage demands in both capacity and performance, ...
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