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Found 15 papers, 2 papers with code
Inductive Bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
The problem of scheduling of workloads onto heterogeneous processors (e. g., CPUs, GPUs, FPGAs) is of fundamental importance in modern datacenters.
Hierarchical Autoscaling for Large Language Model Serving with Chiron
Large language model (LLM) serving is becoming an increasingly important workload for cloud providers.
One Queue Is All You Need: Resolving Head-of-Line Blocking in Large Language Model Serving
$ $Large language models (LLMs) have become an increasingly important workload for cloud providers catering to both enterprise and consumer applications.
Efficient Interactive LLM Serving with Proxy Model-based Sequence Length Prediction
Large language models (LLMs) have been driving a new wave of interactive AI applications across numerous domains.
Watch Out for the Safety-Threatening Actors: Proactively Mitigating Safety Hazards
In this work, we propose a safety threat indicator (STI) using counterfactual reasoning to estimate the importance of each actor on the road with respect to its influence on the AV's safety.
Watch out for the risky actors: Assessing risk in dynamic environments for safe driving
Driving in a dynamic environment that consists of other actors is inherently a risky task as each actor influences the driving decision and may significantly limit the number of choices in terms of navigation and safety plan.
BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statistics
Hardware performance counters (HPCs) that measure low-level architectural and microarchitectural events provide dynamic contextual information about the state of the system.
Application-aware Congestion Mitigation for High-Performance Computing Systems
Netscope has a lower training cost and accurately estimates the impact of congestion on application runtime with a correlation between 0. 7and 0. 9 for common scientific applications.
Distributed, Parallel, and Cluster Computing Networking and Internet Architecture
ML-driven Malware that Targets AV Safety
Ensuring the safety of autonomous vehicles (AVs) is critical for their mass deployment and public adoption.
Inductive-bias-driven Reinforcement Learning For Efficient Schedules in Heterogeneous Clusters
The problem of scheduling of workloads onto heterogeneous processors (e. g., CPUs, GPUs, FPGAs) is of fundamental importance in modern data centers.
Live Forensics for Distributed Storage Systems
We present Kaleidoscope an innovative system that supports live forensics for application performance problems caused by either individual component failures or resource contention issues in large-scale distributed storage systems.
ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents.
Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs
To define and identify a region-of-interest (ROI) in a digital image, the shape descriptor of the ROI has to be described in terms of its boundary characteristics.
P-HGRMS: A Parallel Hypergraph Based Root Mean Square Algorithm for Image Denoising
This paper presents a parallel Salt and Pepper (SP) noise removal algorithm in a grey level digital image based on the Hypergraph Based Root Mean Square (HGRMS) approach.
Results from the Supernova Photometric Classification Challenge
A spectroscopically confirmed subset was provided for training.
Cosmology and Nongalactic Astrophysics
