Intelligent memory manager: towards improving the locality behavior of allocation-intensive applications

Dynamic memory management required by allocation-intensive (i.e., Object Oriented and linked data structured) applications has led to a large number of research trends. Memory performance due to the cache misses in these applications continues to lag in terms of execution cycles as ever increasing CPU-Memory speed gap continues to grow. Sophisticated prefetching techniques, data relocations, and multithreaded architectures have tried to address memory latency. These techniques are not completely successful since they require either extra hardware/software in the system or special properties in the applications. Software needed for prefetching and data relocation strategies, aimed to improve cache performance, pollutes the cache so that the technique itself becomes counter-productive. On the other hand, extra hardware complexity needed in multithreaded architectures decelerates CPU's clock, since “Simpler is Faster”. This dissertation, directed to seek the cause of poor locality behavior of allocation-intensive applications, studies allocators and their impact on the cache performance of these applications. Our study concludes that service functions, in general, and memory management functions, in particular, entangle with application's code and become the major cause of cache pollution. In this dissertation, we present a novel technique that transfers the allocation and de-allocation functions entirely to a separate processor residing in chip with DRAM (Intelligent Memory Manager). Our empirical results show that, on average, 60% of the cache misses caused by allocation and de-allocation service functions are eliminated using our technique. We also show that internal fragmentation, extra memory over-allocated by the allocators, counters special locality of applications. We introduce “hybrid,” an exact fit allocator, which results in 25% cache miss reduction due to minimizing the internal fragmentation. Moreover, this work indicates that external fragmentation, inability to use the existing free space, indirectly affects the execution performance. We propose address ordered and segregrated binary tree allocators that exhibit high storage utilization and moderate execution performance to compare with existing allocators.