Browsing by Author "Panwar, Gagandeep"

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    Towards Using Free Memory to Improve Microarchitecture Performance
    Panwar, Gagandeep (Virginia Tech, 2020-05-18)
    A computer system's memory is designed to accommodate the worst-case workloads with the highest memory requirement; as such, memory is underutilized when a system runs workloads with common-case memory requirements. Through a large-scale study of four production HPC systems, we find that memory underutilization problem in HPC systems is very severe. As unused memory is wasted memory, we propose exposing a compute node's unused memory to its CPU(s) through a user-transparent CPU-OS codesign. This can enable many new microarchitecture techniques that transparently leverage unused memory locations to help improve microarchitecture performance. We refer to these techniques as Free-memory-aware Microarchitecture Techniques (FMTs). In the context of HPC systems, we present a detailed example of an FMT called Free-memory-aware Replication (FMR). FMR replicates in-use data to unused memory locations to effectively reduce average memory read latency. On average across five HPC benchmark suites, FMR provides 13% performance and 8% system-level energy improvement.
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    Utilization-adaptive Memory Architectures
    Panwar, Gagandeep (Virginia Tech, 2024-06-14)
    DRAM contributes significantly to a server system's cost and global warming potential. To make matters worse, DRAM density scaling has not kept up with the scaling in logic and storage technologies. An effective way to reduce DRAM's monetary and environmental cost is to increase its effective utilization and extract the best possible performance in all utilization scenarios. To this end, this dissertation proposes Utilization-adaptive Memory Architectures that enhance the memory controller with the ability to adapt to current memory utilization and implement techniques to boost system performance. These techniques fall under two categories: (i) The techniques under Utilization-adaptive Hardware Memory Replication target the scenario where memory is underutilized and aim to boost performance versus a conventional system without replication, and (ii) The techniques under Utilization-adaptive Hardware Memory Compression target the scenario where memory utilization is high and aim to significantly increase memory capacity while closing the performance gap versus a conventional system that has sufficient memory and does not require compression.