Fast and secure Global-Heap for memory-centric computing

In-memory computing has been widely used to process data quickly in memory, but it is no longer able to cope with the current data explosion. This has occurred because modern computers have structural constraints in terms of both memory capacity and bandwidth, fundamentally limiting the amount of data that can be processed in memory. In order to overcome these limitations, the memory-centric computing concept was proposed, and as an attempt to make this concept feasible, nonvolatile memory, next-generation interconnects, and memory-centric operating system technologies are being actively studied. Although, however, there have been many studies of these concepts, the essential problem of both efficiently and safely using the extremely large memory space has not been completely solved. This paper proposes what is termed Global-Heap which solves the above essential problem, thus supporting the core technology of memory-centric computing. In this technology, a heap-level abstraction which can be switched very lightly between processes is provided in an application-friendly manner as a global resource. Global-Heap also provides an effective means by which safely to use this vast address space based on Intel memory protection keys. We implemented Global-Heap on Linux and developed three useful use cases applicable to data-centric applications. Our primary evaluation shows that Global-Heap enables reorganization-free hashing. In addition, Global-Heap can improve the performance of an in-memory key-value store, Redis, by at least 7.56 times, and it can reduce the running time of graph applications executed in a pipeline approach by at least 48%.