Using SimPoint for accurate and efficient simulation

Abstract

Modern architecture research relies heavily on detailed pipeline simulation. Simulating KeywordsSimPoint, Clustering, Simulation, Fast-forwarding, Sampling SIMPOINTUnderstanding the cycle level behavior of a processor running an application is crucial to modern computer architecture research. To gain this understanding, detailed cycle level simulators are typically employed. Unfortunately, this level of detail comes at the cost of speed, and simulating the full execution of an industry standard benchmark on even the fastest simulator can take weeks to months to complete. This fact has not gone unnoticed in the academic community, and several researchers have started to develop techniques aimed at reducing simulation time.For architecture research it is often necessary to take one instance of a program with a given input, and simulate its performance over many different architecture configurations. The same program binary with the input may be run hundreds or thousands of times to examine how, for example, the effectiveness of a given architecture changes with its size. Our goal in creating SimPoint [1, 2] is to (1) significantly reduce simulation time, (2) provide an accurate characterization of the full program, and (3) to perform the analysis to accomplish the first two goals in a matter of minutes. These goals are met by simulating only a handful of intelligently chosen sections of the full program. When these sections (simulation points) are carefully chosen, it provides an accurate picture of the complete execution of the program and results in highly accurate estimations of performanceThe key to our approach is that for a given program and input, the simulation points only need to be chosen once. This is because we select them using a method that is independent of any particular architecture configuration. The simulation points are selected using a metric that is only based on the code that is executed over time for a program/input pair. Once the sim- ulation points are chosen they can be used for the hundreds or thousands of independent simulations that may be needed, significantly reducing simulation time.To pick the simulation points in [1, 2], we introduce the concept of profiling Basic Block Vectors (BBV) as a way of capturing the important behaviors of the program over time [1]. A Basic Block Vector captures the relative frequency of the code blocks executed during a given portion of execution. After profiling a program with a particular input, we compare the basic block vectors to see how similar they are to one another. Intervals of execution that execute the same code blocks with the same frequency are grouped together into clusters using clustering algorithms from machine learning. We found that sections of execution (represented by basic block vectors) that are grouped into the same cluster have very similar behavior across all the architecture metrics we have examined. Once we break the program into clusters, we pick a single point from each cluster (appropriately weighted) to serve ...

Kill the Program Counter

“…The detailed simulation parameters can be found in Table 2. We collect SimPoint [24] traces from 16 memory intensive SPEC CPU2006 [1] applications, 3 server workloads from CloudSuite [7], and one machine learning workload trace from mlpack [3] that does collaborative filtering on real world data sets [8]. Since our SimPoint methodology does not work with the server workloads (CloudSuite and mlpack), we instead collect the server workload traces after fast-forwarding at least 30B instructions to get past the benchmark's initialization phase.…”

Cooperative Multi-Agent Reinforcement Learning-Based Co-optimization of Cores, Caches, and On-chip Network

“…Sniper supports PinPoint (Patil et al 2004), which is the SimPoint methodology (Sherwood et al 2002;Perelman et al 2003) using the Intel Pin tool (Luk et al 2005). A single 250 million instruction PinPoint (Pinball), which is a representative and repeatable program region, is identified for each Spec2006 benchmark for simulation.…”

Adaptive Cache Warming for Faster Simulations

“…Sampled Simulation. Sampled simulation is used extensively to reduce simulation time, e.g., SMARTS [14], or by the selection of representative samples, e.g., SimPoint needed [12,13,10]. The value of knowing which part of an application to simulate has even made its way into benchmarks, such as with PARSEC benchmark [2], where the applications indicate the regions of interest themselves.…”