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Found 5 papers, 2 papers with code
RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments
Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership.
PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications
A growing number of service providers are exploring methods to improve server utilization and reduce power consumption by co-scheduling high-priority latency-critical workloads with best-effort workloads.
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Our work highlights several novel perspectives and opportunities for implementing randomized algorithms for deep learning on modern CPUs.
IOCA: High-Speed I/O-Aware LLC Management for Network-Centric Multi-Tenant Platform
In this paper, we argue that besides CPU cores, high-speed network I/O is also important for LLC management.
Hardware Architecture Operating Systems
SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems
On the same CPU hardware, SLIDE is over 10x faster than TF.
