Avatars Grow Legs: Generating Smooth Human Motion from Sparse Tracking Inputs with Diffusion Model
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
ACM European Conference on Computer Systems (EUROSYS)
Sharing a MapReduce cluster between users is attractive because it enables statistical multiplexing (lowering costs) and allows users to share a common large data set. However, we find that traditional scheduling algorithms can perform very poorly in MapReduce due to two aspects of the MapReduce setting: the need for data locality (running computation where the data is) and the dependence between map and reduce tasks.
We illustrate these problems through our experience designing a fair scheduler for MapReduce at Facebook, which runs a 600-node multi-user data warehouse on Hadoop. We developed two simple techniques, delay scheduling and copy-compute splitting, which improve throughput and response times by factors of 2 to 10. Although we focus on multi-user workloads, our techniques can also raise throughput in a single-user, FIFO workload by a factor of 2.
Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke, Ali Thabet, Artsiom Sanakoyeu
Bilge Acun, Benjamin Lee, Fiodar Kazhamiaka, Kiwan Maeng, Manoj Chakkaravarthy, Udit Gupta, David Brooks, Carole-Jean Wu
Ilkan Esiyok, Pascal Berrang, Katriel Cohn-Gordon, Robert Künnemann