Adapting to Memory Pressure from within Scientific Applications on Multiprogrammed COWs

Dismal performance often results when the memory requirements of a process exceed the physical memory available to it. Moreover, significant throughput reduction is experienced when this process is part of a synchronous parallel job on a non-dedicated computational cluster. A possible solution is to develop programs that can dynamically adapt their memory usage according to the current availability of physical memory. We explore this idea on scientific computations that perform repetitive data accesses. Part of the program's data set is cached in resident memory, while the remainder that cannot fit is accessed in an "out-of-core" fashion from disk. The replacement policy can be user defined. This allows for a graceful degradation of performance as memory becomes scarce. To dynamically adjust its memory usage, the program must reliably answer whether there is a memory shortage or surplus in the system. Because operating systems typically export limited memory information, we develop a parameter- Work supported by the National Science Foundation (ITR/ACS-0082094 and ITR/AP-0112727), a DOE computational sciences graduate fellowship, and the College of William and Mary

Efficient caching algorithms for memory management in computer systems

As disk performance continues to lag behind that of memory systems and processors, fully utilizing memory to reduce disk accesses is a highly effective effort to improve the entire system performance. Furthermore, to serve the applications running on a computer in distributed systems, not only the local memory but also the memory on remote servers must be effectively managed to minimize I/O operations. The critical challenges in an effective memory cache management include: (1) Insightfully understanding and quantifying the locality inherent in the memory access requests; (2) Effectively utilizing the locality information in replacement algorithms; (3) Intelligently placing and replacing data in the multi-level caches of a distributed system; (4) Ensuring that the overheads of the proposed schemes are acceptable.;This dissertation provides solutions and makes unique and novel contributions in application locality quantification, general replacement algorithms, low-cost replacement policy, thrashing protection, as well as multi-level cache management in a distributed system. First, the dissertation proposes a new method to quantify locality strength, and accurately to identify the data with strong locality. It also provides a new replacement algorithm, which significantly outperforms existing algorithms. Second, considering the extremely low-cost requirements on replacement policies in virtual memory management, the dissertation proposes a policy meeting the requirements, and considerably exceeding the performance existing policies. Third, the dissertation provides an effective scheme to protect the system from thrashing for running memory-intensive applications. Finally, the dissertation provides a multi-level block placement and replacement protocol in a distributed client-server environment, exploiting non-uniform locality strengths in the I/O access requests.;The methodology used in this study include careful application behavior characterization, system requirement analysis, algorithm designs, trace-driven simulation, and system implementations. A main conclusion of the work is that there is still much room for innovation and significant performance improvement for the seemingly mature and stable policies that have been broadly used in the current operating system design