Visualizing Dynamic Memory Allocations

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Visualizing Dynamic Memory Allocations

• Sergio Moreta and Alexandru Telea
• Department of Mathematics and Computer Science
• Technische Universiteit Eindhoven, the
  Netherlands
• s.moreta_at_student.tue.nl, alext_at_win.tue.nl
• By
• Rizal Mohd Nor
• rnor_at_cs.kent.edu

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Dr. Alexandru C. Telea Sergio
Moreta

• Department of Mathematics and Computing Science,
  Eindhoven University of Technology The
  Netherlands
• Research Area
• scientific visualisation
• information visualisation
• multiscale visualisation
• interactive systems design
• computer imaging
• object-oriented and component-oriented software
  design
• compiler/interpreter design
• fact extraction
• reverse engineering

Department of Mathematics and Computing Science,
Eindhoven University of Technology The
Netherlands Graduate Student
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Motivation

• To present a visualization tool for dynamic
  memory allocation, information obtained from
  instrumenting the runtime allocator used by C
  programs.
• The goal is to convey insight in the dynamic
  behavior of the allocator.
• The purpose is to help the allocator designers
  understand the performance and workings of the
  allocator.

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Introduction

• Dynamic memory allocators is a component of the
  runtime that is responsible for providing memory
  blocks, as offered by the malloc C function and
  new C operator.
• Allocated blocks are returned to the runtime, via
  explicit calls to free (C) or delete (C) or via
  garbage collection (java)
• Several performance metrics are relevant
• The allocator should minimize memory
  fragmentation
• the allocator should minimize waste
• the allocator should provide a good response time
  for both allocation and free requests for a wide
  mix of scenarios

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Problem

• Understanding log data to detect sub-optimal
  performance, and when and why this occurs, is
  difficult.
• Typical logs can easily contain hundreds of
  thousands of high frequency events.
• Examples are finding incorrect allocation/dealloca
  tion sequences which could lead to memory leaks,
  dangling pointers, or uninitialized memory reads.

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Core Visualization Design(0)

• Design is driven by several goals scalability,
  limited cluttering, insight into fine-scale
  behavior, intuitiveness, and ease of use.
• Use 2D instead of 3D. Justification in thesis
  paper not to use 3D
• Object occlusion.
• Awkward navigation.
• Awkward selection.
• Obscured relations.
• Slow rendering.
• Using Adapted Rainbow Color Map. Unfortunately,
  normal humans can only distinguish 6-10 colors.

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Interactive tool for visualizing dynamic memory
allocation.
The Occupancy bar shows, using a blue (low) to
red (high) color map, the evolution in time of
the total memory allocated in the bin shown in
the main view.
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Core Visualization Design(1)

• Showing Block Structure
• color blocks using flat shading, same-color
  neighbor blocks cannot be distinguished
• Drawing line borders works only for zoomed-in
  views, where the block sizes are larger than
  several pixels.
• Solve problem by overlaying each block using a
  so-called shaded cushion.

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Core Visualization Design(2)

• Showing Subpixel structure
• Many (de)allocation events can occur at very high
  frequencies
• The size of an event rectangle can easily become
  smaller than one pixel.
• Simply drawing the rectangles produces wrong
  effects, as a pixel will show a single rectangle,
  or even none, depending on round-off errors.
• Solution, anti-aliasing, a changes visibility,
  and Base changes pixel fractions

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Core Visualization Design(3)

• Visualizing Activity in the Occupancy Bar
• The occupancy bar has the same problem as the
  main view due to high-frequency events.
• An improvement is to draw black bars outlining
  no-activity zones which are larger than a few
  pixels
• Also add cushioning and anti-aliasing

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Core Visualization Design(4)

• Hierarchical Agglomerative Clustering
• provide a multi-level partitioning of data.
• Dynamic partitioning of data serves as a visual
  aid for supporting the user in detecting the
  artifacts high-level structure.

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Core Visualization Design(5)

• Correlation of Multiple Views
• An important task is to compare different bins to
  detect possible unbalances, which can be further
  corrected by adjusting the allocator parameters
• Just displaying bin views in a grid layout does
  not allow users to easily see if certain values
  of interest are indeed correlated.
• Solution, use cushion cursors

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Application
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Application

• By comparing Bins and heap occupancy
  visualization, it is possible to see certain
  patterns.
• Particularly, questions such as the ones below
  can be answered.
• Are allocations always done from low to high
  addresses?
• Is activity correlated with the memory allocating
  functions?

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My thoughts

• How does this help program comprehension?
• Not much.
• Alternative ways for visualizing abundance of
  data that are time sensitive.
• Understanding memory usage for limited memory
  devices like embedded systems.
• What else can we use it for?
• Maybe to understand patterns in high frequency
  changes between fixations for eye tracking
  system.
• Clustering method used a certain distance metric
  to cluster objects, this idea can be applied in
  fish view to reduce or amplify context view.