Search Results for author:
Found 4 papers, 2 papers with code
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.
