1. [Publications](/publications) 2. Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs # Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs ![](/sites/default/files/styles/wide/public/publications/choukse.isca2020.png?itok=2zNzAaab) GPUs accelerate high-throughput applications, which require orders-of-magnitude higher memory bandwidth than traditional CPU-only systems. However, the capacity of such high-bandwidth memory tends to be relatively small. Buddy Compression is an architecture that makes novel use of compression to utilize a larger buddy-memory from the host or disaggregated memory, effectively increasing the memory capacity of the GPU. Buddy Compression splits each compressed 128B memory-entry between the high-bandwidth GPU memory and a slower-but-larger buddy memory such that compressible memory-entries are accessed completely from GPU memory, while incompressible entries source some of their data from off-GPU memory. With Buddy Compression, compressibility changes never result in expensive page movement or re-allocation. Buddy Compression achieves on average 1.9× effective GPU memory expansion for representative HPC applications and 1.5× for deep learning training, performing within 2% of an unrealistic system with no memory limit. This makes Buddy Compression attractive for performance-conscious developers that require additional GPU memory capacity. ## Authors Esha Chouske (Microsoft) [Michael B. Sullivan](/person/mike-sullivan) [Mike O'Connor](/person/mike-o-connor) Mattan Erez (University of Texas - Austin) Jeff Pool (NVIDIA) [David Nellans](/person/david-nellans) [Steve Keckler](/person/stephen-keckler) ## Publication Date Wednesday, June 3, 2020 ## Published in [International Symposium on Computer Architecture (ISCA)](https://ieeexplore.ieee.org/document/9138915) ## Research Area [Artificial Intelligence and Machine Learning ](/research-area/machine-learning-artificial-intelligence) [Computer Architecture](/research-area/computer-architecture) [High Performance Computing](/research-area/high-performance-computing) ## External Links [IEEE Digital Library](https://ieeexplore.ieee.org/document/9138915) ## Uploaded Files [Published manuscript](https://research.nvidia.com/sites/default/files/pubs/2020-06_Buddy-Compression%3A-Enabling//chouske.isca2020.pdf "Open file in new window")1.28 MB ## Copyright This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to .