97 citations · 109 across the 5 of their papers we have counts for
17 papers
PIM-tree: A Skew-resistant Index for Processing-in-Memory
Hongbo Kang, Yiwei Zhao, Guy E. Blelloch +4
The performance of today's in-memory indexes is bottlenecked by the memory latency/bandwidth wall. Processing-in-memory (PIM) is an emerging approach that potentially mitigates thi…
Spatial Locality and Granularity Change in Caching
Nathan Beckmann, Phillip B Gibbons, Charles McGuffey
Caches exploit temporal and spatial locality to allow a small memory to provide fast access to data stored in large, slow memory. The temporal aspect of locality is extremely well…
Cortex: A Compiler for Recursive Deep Learning Models
Pratik Fegade, Tianqi Chen, Phillip B. Gibbons +1
Optimizing deep learning models is generally performed in two steps: (i) high-level graph optimizations such as kernel fusion and (ii) low level kernel optimizations such as those…
DriftSurf: A Risk-competitive Learning Algorithm under Concept Drift
Ashraf Tahmasbi, Ellango Jothimurugesan, Srikanta Tirthapura +1
When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model…
Advances and Open Problems in Federated Learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent +56
Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a cen…
Sage: Parallel Semi-Asymmetric Graph Algorithms for NVRAMs
Laxman Dhulipala, Charlie McGuffey, Hongbo Kang +4
Non-volatile main memory (NVRAM) technologies provide an attractive set of features for large-scale graph analytics, including byte-addressability, low idle power, and improved mem…