Adaptive dynamic memory allocators by estimating application workloads

Abstract

Modern applications are becoming more complex and dynamic and try to efficiently utilize the amount of available resources on the computing platforms. Efficient memory utilization is a key challenge for application developers, especially since memory is a scarce resource and often becomes systems bottleneck. Thus, the developers can resort to dynamic memory management, i.e., dynamic memory allocation and de-allocation, to efficiently utilize the memory resources. A high-performance adaptive memory allocator is presented in this paper. A memory allocator helps applications to manage more efficiently the memory space that operating systems bestow to them. In our approach, we tune the memory allocator at runtime by predicting the amount of memory to be requested. Experimental results obtained using applications from the PARSEC benchmark suite and dmmlib, a memory allocator framework written in C. Results show that adaptive memory allocators can improve the fragmentation problems leading to a more efficient memory usage.