Active cache emulator

  • Authors:
  • Eriko Nurvitadhi;Jumnit Hong;Shih-Lien Lu

  • Affiliations:
  • Electrical and Computer Engineering Department, Carnegie Mellon University, Pittsburgh, PA;Infrastructure Processor Division, Intel Corporation, Hillsboro, OR;Microarchitecture Research Laboratory, Intel Corporation, Hillsboro, OR

  • Venue:
  • IEEE Transactions on Very Large Scale Integration (VLSI) Systems
  • Year:
  • 2008

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Abstract

This paper presents the active cache emulator (ACE), a novel field-programmable gate-array (FPGA)-based emulator that models an L3 cache actively and in real-time. ACE leverages interactions with its host system to model the target system. Unlike most existing FPGA-based cache emulators that collect only memory traces from their host system, ACE provides feedback to its host by injecting delays to time dilate the host system such that it experiences hit/miss latencies of the emulated cache. Such active emulation expands the context of performance evaluations by allowing measurements of system performance metrics (e.g., CPI, operations per second, frame rate) in addition to the typical cache-specific performance metrics (e.g., miss ratio) provided by existing emulators. ACE is designed to interface with a front-side bus (FSB) of a typical Pentium-based PC system. ACE utilizes the FSB snoop stall mechanism to inject delays into the system. At present, ACE is implemented using a Xilinx XC2V6000 FPGA running at 66 MHz, the same speed as its host's FSB. Verification of ACE includes using the cache calibrator and RightMark memory analyzer software to confirm proper detection of the emulated cache by the host system, and comparing ACE results with SimpleScalar software simulations. Finally, ACE is used to study L3 caches for compute-intensive, throughput-oriented, and real-time gaming benchmarks (SPEC-CPU2000, SPEC-JBB2000, Quake3). The study shows that analyzing only cache-specific metrics, as done by existing L3 cache studies with FPGA emulators, is insufficient. Active emulation mitigates this issue by providing a broader performance view, allowing researchers make better research conclusion.