Visual Exploration of Memory Traces and Call Stacks

Gralka, Patrick; Schulz, Christoph; Reina, Guido; Weiskopf, Daniel; Ertl, Thomas

doi:10.1109/vissoft.2017.15

Cited by 8 publications

(2 citation statements)

References 25 publications

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“…Many of these visualizations are designed for view traces of parallel programs [24,37,41]. There also exists a significant body of trace visualizations that use icicle plots and flame graphs as the primary visualization [7,17,24,33,41]. Anteater presents its trace in a plot similar to icicle plots and flame graphs because these plots are common and wellunderstood for visualizing execution traces.…”

Section: Related Workmentioning

confidence: 99%

Anteater: Interactive Visualization of Program Execution Values in Context

Faust,

Isaacs,

Bernstein

et al. 2019

Preprint

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Fig. 1. A programmer investigates a bug in their code. One common practice, shown in the top row, is to instrument the program manually to collect suspicious variables (in this case x), and print out their values. Manual instrumentation, however, is itself error-prone and leaves the programmer responsible for sifting through values. Another common practice, shown in the second row, is to use a debugger to stop the execution of the program automatically and view each assignment of x individually. However, breakpoint-debugging tends to provide only a narrow, one-at-a-time view of the values. Anteater, shown in the bottom row, automatically instruments the code to track variables along with the context of their execution structure. It presents the programmer with interactive visualizations that give a global view of values, which allow for easy detection of erroneous values as well as interactions to narrow down the visualizations to inspect specific values. We provide a demo of Anteater in action as supplemental material.

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Section: Related Workmentioning

confidence: 99%

Anteater: Interactive Visualization of Program Execution Values in Context

Faust,

Isaacs,

Bernstein

et al. 2019

Preprint

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“…Many applications need to know the running states of specific tasks. ese applications include anomaly detection on data centers [3,4], compiler optimization using method stacks [5,6], and hot spots detections [7,8].…”

Section: Introductionmentioning

confidence: 99%

Probability Analysis to Improve the Confidence in Profiling Accuracy

Wen

Cheng

Lin

et al. 2021

Mathematical Problems in Engineering

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Performance profiling for the system is necessary and has already been widely supported by hardware performance counters (HPC). HPC is based on the registers to count the number of events in a time interval and uses system interruption to read the number from registers to a recording file. The profiled result approximates the actual running states and is not accurate since the profiling technique uses sampling to capture the states. We do not know the actual running states before, which makes the validation on profiling results complex. Jianwei YinSome experiments-based analysis compared the running results of benchmarks running on different systems to improve the confidence of the profiling technique. But they have not explained why the sampling technique can represent the actual running states. We use the probability theory to prove that the expectation value of events profiled is an unbiased estimation of the actual states, and its variance is small enough. For knowing the actual running states, we design a simulation to generate the running states and get the profiled results. We refer to the applications running on production data centers to choose the parameters for our simulation settings. Comparing the actual running states and the profiled results shows they are similar, which proves our probability analysis is correct and improves our confidence in profiling accuracy.

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