Feasibility of decoupling memory management from the execution pipeline
Section snippets
Introduction and motivation
Modern programming languages often permit complex dynamic memory allocation and garbage collection. Such features provide computer systems architects with a challenge of reducing the overheads due to memory management functions. The challenge is further exacerbated by the ever-increasing gap between memory and processor speeds. Some researchers chose to employ custom memory allocation methods into their systems; however, it has been shown that such custom allocators generally do not improve
Related research
Several research threads, including custom allocators, and hardware implementation of memory management function, have influenced our research.
Dynamic memory management is an important problem studied by researchers for the past several decades. Modern programming languages and applications are driving the need for more efficient implementations of memory management functions, in terms of both memory usage and execution performance. Several researchers have proposed and implemented custom
Experimental framework
To evaluate the potential for decoupling memory management, we constructed experiments to reflect conditions as close to real execution environments as possible. We have identified and controlled experimental parameters such as machine model(s), appropriate benchmarks, and statistical attributes of interest. In this section we describe our methodology and the selection of benchmarks.
Experiment results
In this section, we report the results of our experiments. We discuss both the execution performance and cache behavior resulting from decoupling of memory management functions.
Simple optimization of the decoupled memory manager
In general, a hardware implementation of any function should require fewer cycles than a corresponding software implementation. The performance of a hardware implementation of Lea’s allocator can also be improved for applications such as voronoi and treeadd which make bursts of malloc calls. In such cases the (hardware) allocator could predict that the next malloc request would be for the same sized object as the previous request. Thus the allocator could pre-allocate similar-sized objects. If
Conclusions and future research
In this study we have shown that decoupling memory management functions from the processing pipeline can lead to improved performance. Several features impact performance of modern architectures. Among these are out-of-order execution, speculative execution, and cache hierarchies. Application characteristics in terms of memory usage, distribution of allocation requests over the lifetime of the application, and the sizes of objects requested also impact performance. Decoupling eliminates a
Wentong Li received his MS degree in Computer Science from the University of North Texas. He is currently completing his PhD in Computer Science at the University of North Texas. He is employed by Turn, Inc. as staff software engineer. His research interests cover the areas of computer architecture, machine learning and information retrieval.
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Cited by (0)
Wentong Li received his MS degree in Computer Science from the University of North Texas. He is currently completing his PhD in Computer Science at the University of North Texas. He is employed by Turn, Inc. as staff software engineer. His research interests cover the areas of computer architecture, machine learning and information retrieval.
Mehran Rezai received BS and MS degrees in Electrical Engineering from the University of Alabama in Huntsville and a PhD in Computer Science from the University of North Texas. He worked as a visiting faculty member at the University of Texas at Arlington. He is currently working as a software consultant in Washington, DC area.
Krishna M. Kavi is currently a professor and the Chair of Computer Science and Engineering Department at the University of North Texas. Previously, he was the Eminent Scholar Chair Professor of Computer Engineering at the University of Alabama in Huntsville from 1997 to 2001. He was on the faculty at the University of Texas at Arlington from 1982 to1997. He was a program manager at the US National Science Foundation from 1993 to 1995. He has extensive research record covering intelligent memory systems, multithreaded and decoupled architectures, dataflow model of computation, scheduling and load-balancing.
Afrin Naz is currently completing her PhD in Computer Science at the University of North Texas. She received her MS in Computer Science from Midwestern State University, Wichita Falls, Texas. She is a member of UPSILON PI EPSILON Chapter of Texas at Midwestern State University. She is also the recipient of multi-cultural scholastic award of University of North Texas. She will start her academic career as an Assistant Professor at Drake University in Iowa, in Fall 2007. Her research interest includes Computer Architecture, Compilers and Embedded System designs.
Philip Sweany, associate professor in UNTs Computer Science and Engineering Department, maintains a research focus in both compiler optimization for architectures exhibiting fine-grained parallelism and application of compiler optimization algorithms to automated synthesis of net-centric systems.