… In summary, GPU workers do not need to synchronize on memory management as they only
need to receive all the memory management information at the beginning of each decoding …

… However, the authors do not focus on the core problem of memory management and do not
explore different algorithms that can solve this problem in the most effective way. (Chen et al.…

… Additionally, this work investigates the adoption of two memory management strategies
for dataflow applications: Copy-on-Write (CoW) and Non-Temporal Memory transfers (NTM). …

… In this section, we introduce the unified program abstraction for implementing SmartPool
and AutoSwap, which could be adopted into an existing deep learning framework with ease. …

Abstract: Due to the recent trend of software intelligence in the Fourth … memory capacity
efficiently for deep learning workloads becomes important. In this paper, we analyze memory …

… This paper presents two efficient memory management … , to address the memory aspects
of the dataflow model: copy-on… Sniper: exploring the level of abstraction for scalable and …

… GPU memory management based on computation graph and characteristics of diferent
layers. However, a layer of neural networks is a high-level computation abstraction composed of …

… Abstract. In the wake of extensible … Memory Management solution has to be very fast as
well as stable. This paper proposes a Memory Management Scheme, which minimises memory …

… memory bandwidth. To remedy these shortcomings, we propose and implement a general
purpose OS-integrated multi-level memory management … an abstract example of the memory …

… created when no external call is executed, overall memory management was improved,
supporting programs which require more memory to be allocated. This thesis focused on solving …