Detailed architectural simulators suffer from a long development cycle and extremely long evaluation times. This longstanding problem is further exacerbated in the multi-core processor era. Existing solutions address the simulation problem by either sampling the simulated instruction stream or by mapping the simulation models on FPGAs; these approaches achieve substantial simulation speedups while simulating performance in a cycle-accurate manner. This paper proposes interval simulation which takes a completely different approach: interval simulation raises the level of abstraction and replaces the core-level cycle-accurate simulation model by a mechanistic analytical model. The analytical model estimates core-level performance by analyzing intervals, or the timing between two miss events (branch mispredictions and TLB/cache misses); the miss events are determined through simulation of the memory hierarchy, cache coherence protocol, interconnection network and branch predictor. By raising the level of abstraction, interval simulation reduces both development time and evaluation time. Our experimental results using the SPEC CPU2000 and PARSEC benchmark suites and the M5 multi-core simulator, show good accuracy up to eight cores (average error of 4.6% and max error of 11% for the multi-threaded full-system workloads), while achieving a one order of magnitude simulation speedup compared to cycle-accurate simulation. Moreover, interval simulation is easy to implement: our implementation of the mechanistic analytical model incurs only one thousand lines of code. Its high accuracy, fast simulation speed and ease-of-use make interval simulation a useful complement to the architect's toolbox for exploring system-level and high-level micro-architecture trade-offs.

Developing fast chip multiprocessor simulation techniques is a challenging problem. Solving this problem is especially valuable for design space exploration purposes during the early stages of the design cycle where a large number of design points need to be evaluated quickly. This paper studies statistical simulation as a fast simulation technique for chip multiprocessor (CMP) design space exploration. The idea of statistical simulation is to measure a number of program execution characteristics from a real program execution through profiling, to generate a synthetic trace from it, and simulate that synthetic trace as a proxy for the original program. The important benefit is that the synthetic trace is much shorter compared to a real program trace, which leads to substantial simulation speedups. This paper enhances state-of-the-art statistical simulation: 1) by modeling the memory address stream behavior in a more microarchitecture-independent way and 2) by modeling a program's time-varying execution behavior. These two enhancements enable accurately modeling resource conflicts in shared resources as observed in the memory hierarchy of contemporary chip multiprocessors when multiple programs are coexecuting on the CMP. Our experimental evaluation using the SPEC CPU benchmarks demonstrates average prediction error of 7.3 percent across a range of CMP configurations while varying the number of cores and memory hierarchy configurations.

Microprocessor design is both complex and time consuming: exploring a huge design space for identifying the optimal design under a number of constraints is infeasible using detailed architectural simulation of entire benchmark executions. Statistical simulation is a recently introduced approach for efficiently culling the microprocessor design space. The basic idea of statistical simulation is to collect a number of important program characteristics and to generate a synthetic trace from it. Simulating this synthetic trace is extremely fast as it contains only a million instructions. This paper improves the statistical simulation methodology by proposing accurate memory data flow models. We propose 1) cache miss correlation, or measuring cache statistics conditionally dependent on the global cache hit/miss history, for modeling cache miss patterns and memory-level parallelism, 2) cache line reuse distributions for modeling accesses to outstanding cache lines, and 3) through-memory read-after-write dependency distributions for modeling load forwarding and bypassing. Our experiments using the SPEC CPU2000 benchmarks show substantial improvements compared to current state-of-the-art statistical simulation methods. For example, for our baseline configuration, we reduce the average instructions per cycle (IPC) prediction error from 10.9 to 2.1 percent; the maximum error observed equals 5.8 percent. In addition, we show that performance trends are predicted very accurately, making statistical simulation enhanced with accurate data flow models a useful tool for efficient and accurate microprocessor design space explorations.

This paper explores statistical simulation as a fast simulation technique for driving chip multiprocessor (CMP) design space exploration. The idea of statistical simulation is to measure a number of important program execution characteristics, generate a synthetic trace, and simulate that synthetic trace. The important benefit is that a synthetic trace is very small compared to real program traces. This paper advances statistical simulation by modeling shared resources, such as shared caches and off-chip bandwidth. This is done (i) by collecting cache set access probabilities and per-set LRU stack depth profiles, and (ii) by modeling a programpsilas time-varying execution behavior in the synthetic trace. The key benefit is that the statistical profile is independent of a given cache configuration and the amount of multiprocessing, which enables statistical simulation to model conflict behavior in shared caches when multiple programs are co-executing on a CMP. We demonstrate that statistical simulation is both accurate and fast with average IPC prediction errors of less than 5.5% and simulation speedups of 40X to 70X compared to the detailed simulation of 100M-instruction traces. This makes statistical simulation a viable tool for CMP design space exploration.

Microprocessor design is a very complex and time-consuming activity. One of the primary reasons is the huge design space that needs to be explored in order to identify the optimal design given a number of constraints. Simulations are usually used to explore these huge design spaces, however, they are fairly slow. Several hundreds of billions of instructions need to be simulated per benchmark; and this needs to be done for every design point of interest.Recently, statistical simulation was proposed to efficiently cull a huge design space. The basic idea of statistical simulation is to collect a number of important program characteristics and to generate a synthetic trace from it. Simulating this synthetic trace is extremely fast as it contains a million instructions only.This paper improves the statistical simulation methodology by proposing accurate memory data flow models. We model (i) load forwarding, (ii) delayed cache hits, and (iii) correlation between cache misses based on path info. Our experiments using the SPEC CPU2000 benchmarks show a substantial improvement upon current state-of-the-art statistical simulation methods. For example, for our baseline configuration we reduce the average IPC prediction error from 10.7% to 2.3%. In addition, we show that performance trends are predicted very accurately, making statistical simulation enhanced with accurate data flow models a useful tool for efficient and accurate microprocessor design space explorations.