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 $1.9 imes$ effective GPU memory expansion for representative HPC applications and $1.5 imes$ 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.
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机译：GPU加速了高吞吐量应用程序，与传统的仅使用CPU的系统相比，这些应用程序需要更高数量级的内存带宽。但是，这种高带宽存储器的容量往往相对较小。 Buddy Compression是一种架构，它新颖地利用了压缩功能来利用来自主机或分类内存的更大的Buddy内存，从而有效地增加了GPU的内存容量。 Buddy Compression在高带宽GPU内存和速度较慢但较大的Buddy内存之间拆分每个压缩的128B内存条目，以便完全从GPU内存访问可压缩的内存条目，而不可压缩的条目则从GPU外获取其某些数据记忆。使用Buddy Compression，可压缩性的更改永远不会导致昂贵的页面移动或重新分配。对于典型的HPC应用程序，Buddy Compression平均可实现$ 1.9 \ times $的有效GPU内存扩展，而对于深度学习培训则可实现$ 1.5 \ times $的性能，在不具有内存限制的不切实际系统的2％内实现。这使Buddy Compression对于需要额外的GPU内存容量的注重性能的开发人员具有吸引力。
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