Graphic Chartiles and High Performance Computing
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1 Center for Information Services and High Performance Computing (ZIH) Leistungsanalyse von Rechnersystemen Data Presentation Nöthnitzer Straße 46 Raum 1026 Tel Holger Brunst (holger.brunst@tu-dresden.de) Matthias S. Mueller (matthias.mueller@tu-dresden.de) Center for Information Services and High Performance Computing (ZIH) Summary of Previous Lecture Nöthnitzer Straße 46 Raum 1026 Tel Holger Brunst (holger.brunst@tu-dresden.de) Matthias S. Mueller (matthias.mueller@tu-dresden.de)
2 Summary of Previous Lecture: Part I Monitoring Techniques Tool to observe the activities on a system Terminology: Event, Trace, Overhead, Domain, Input Rate, Resolution, Input Width System implementation levels: Software, Hardware, Firmware, Hybrid Trigger mechanisms: Event-driven, timer-driven Data display: On-line, batch/post-mortem Interval timers Most fundamental measuring tool, based on counting clock pulses Execution time, forms basis for sampling Hardware, software Time rollover 3 Summary of Previous Lecture: Part II Program Execution Monitors: PC Sampling Basic Block Counting Indirect Strategies Tracing Instrumentation Manual (source based) Software Exceptions Emulation Binary Library Compiler Tools and their formats 4
3 Center for Information Services and High Performance Computing (ZIH) Data Presentation Nöthnitzer Straße 46 Raum 1026 Tel Holger Brunst (holger.brunst@tu-dresden.de) Matthias S. Mueller (matthias.mueller@tu-dresden.de) Data Presentation It is not what you say, but how you say it 6
4 Outline Motivation Variable Types to be displayed Chart types Guidelines Common mistakes Chart games Exemplary chart types in the Vampir performance tool 7 Presentation of Final Results Eventual aim of performance analysis: Help in decision making Analysis results which cannot be understood by decision makers are worthless Results need to presented and clearly and simply as possible Prudent use of words, pictures, and graphs Common and specialized graphic charts: line charts, bar charts, pie chars and histograms Gantt charts, timeline charts, Kiviat graphs, and Schumacher charts Reasons for graphical presentation: A picture is worth a thousand words (if it is well designed) Saves readers time and is more concisely Arouses more interest than a textual representation Easier to grasp the main points of the study Clarify a point, underline a conclusion, and summarize the study 8
5 Types of Variables to be Displayed: Qualitative Type of variables affects the choice of graphic chart Qualitative and quantitative variables Qualitative variables: Also referred to as categorical variables Have states, levels, or categories defined by mutual exclusive and exhaustive subclasses Usually expressed in words Can be ordered and unordered Examples: A computer type with three subclasses supercomputers, desktop computers, and microcomputers is an ordered qualitative variable A workload type variable with three subclasses scientific workload, engineering workload, and educational workload is an unordered qualitative variable 9 Types of Variables to be Displayed: Quantitative Quantitative variables: Are expressed numerically Discrete and continuous variables Discrete variables: Values are countable Examples: Number of processors, size of disk blocks, etc. Continuous variables: Values can be uncountable Often referred to as: Real values in programming languages Examples: Response time of job, weight of a laptop, footprint of computer system installation, etc. 10
6 Types of Variables to be Displayed: Hierarchy Variables Qualitative/Categorical Quantitative Ordered Unordered Discrete Continuous The variable type determines the type of graphic chart to be used Line chart: Shows the relationship of two continuous variables Bar chart: Qualitative variable on x-axis quantitative on y-axis Histogram: Table that shows what proportion of cases fall in each several specified categories Pie Chart: Slices represent qualitative variables. Its size is a quantitative variable. Normalized. 11 Guidelines: Minimum Effort Requirement A graphic chart should require as few reading effort as possible to understand the message behind the chart Example: Graph with legend box or direct labeling Legend box: Requires more effort to be read Direct labeling: Is preferable, particularly for a larger number of curves Also applies to column/bar charts 12
7 Guidelines: Minimum Reading Effort B C A 13 Guidelines: Maximum Information Graph should be self-contained Use key words in stead of symbols Symbols require extra attention from the reader to make sense Axes should be as informative as possible Be informative: Daily system utilization versus System utilization Include units in the labels Bandwidth in bytes per second is better than Bandwidth 14
8 Guidelines: Minimum Ink Present maximum information with as little ink as possible Don t clutter chart with unnecessary information Example: Grid lines unless they are required to accurately read values Fancy 3D effects, shades, jumping numbers etc. Prefer chart that provides more information on the same data Inverse data is sometimes more meaningful 15 Guidelines: Minimum Ink 16
9 Guidelines: Commonly Accepted Practices Present what people expect. Follow standard conventions. Chart origin at (0,0) in the bottom left corner Plot independent variable, i.e. the cause, along the x-axis Plot dependent variable, i.e. the effect, along the y-axis Use linear scales whenever possible Increase scales from left to right or bottom to top Equal scale divisions Deviations from these standard conventions are permitted but require: extra explanations from the presenter extra attention and understanding from the reader Thus, deviations only when necessary 17 Guidelines: Avoid Ambiguity Show Coordinate axes Scale divisions Origin Identify individual Curves Graphs The graph should be easy to read Avoid multiple variables in the same chart 18
10 Guidelines: Checklist, Part 1 Source: R. Jain, The Art of Computer Systems Performance Analysis Make sure you can answer all questions with yes Are both coordinate axes shown and labeled? Are the axes labels self-explanatory and concise? Are the scales and divisions shown on both axes? Are the minimum and maximum of the ranges shown on the axes appropriate to present the maximum information? Is the number of curves reasonably small? (usually not more than six) Do all graphs use the same scale? (multiple scales on the same chart are confusing) Is there no curve that can be removed without reducing the information? Are the curves on a line chart individually labeled? Are the cells in a bar chart individually labeled? Are all symbols on the graph accompanied by appropriate explanations If the curves cross, are the line patterns different to avoid confusion 19 Guidelines: Checklist, Part 2 Are the units of measurement indicated? Is the horizontal scale increasing from left to right? Is the vertical scale increasing from bottom to top? Are the grid lines aiding in reading the curves? (Turn them off, if not) Does this whole chart add to information available to the reader? Are the scales contiguous? (Avoid breaks in the scale) Is the order of bars in a bar chart systematic? (alphabetic, temporal, or best to worse preferred over random ordering) If the vertical axis represents random quantity, are confidence intervals shown? Are there no curves, symbols, or texts on the graph that can be removed without affecting the information? Is there a title for the whole chart? Is the chart title self-explanatory and concise? Do the variables plotted on this chart give more information that other alternatives? Does the chart clearly bring out the intended message? Is the figure referenced and discussed in the text of the report? 20
11 Common Mistakes: Too Many Alternatives or Numbers Readers cannot grasp more than five to seven messages at the same time Avoid charts with too many curves, bars, or components Rules of thumb: Line chart: at most 6 curves Column or bar chart: at most 10 bars Pie chart: at most 8 components Histogram: Each bar should at least represent 5 five data points Rules can be overridden if it helps to make the point 21 Common Mistakes: Multiple y-variables on Single Chart Combination of multiple y-variables on a single chart is confusing Leaves association of curves and corresponding scales to the reader Individual graphs are better to get the intended message However, standard practice to meet the length requirements of papers Throughput 20 Utilization Response time Throughput 22
12 Common Mistakes: Using Symbols in Place of Text Don t use symbols. Prefer textual labels Otherwise: Reader has to find the meaning of each symbol in the report Symbols saves a writer s time but consumes reader s time Result: Reader will skip the figure or the entire report μ = 1 μ = 3 1 job/sec 3 jobs/sec μ = 2 2 jobs/sec 23 Common Mistakes: Miscellaneous Remove information that detracts reader from the message Example: Grid line on a line chart only if reader is expected to read precise values Match the granularity of the grid to the accuracy required Automatic selection of scales in programs In Practice: Often necessary to adjust the default selection Necessary if a certain part of the visible range does not add further information Using line charts where column or bar charts apply: The lines between points indicate that intermediate value can be interpolated This does not apply to qualitative or discrete quantitative values often shown on the category axis Example: Speed measurements for a set of different processors 24
13 Pictorial Games: Nonzero Origins and Scaling Normally, both axes of a graph should meet at zero. By moving the origin and scaling the graph, the same data can be shown as: MINE is much better than YOURS (left figure) MINE and YOURS are almost the same (middle figure) Three-quarter-high rule: Highest point be at least three quarters of the horizontal offset of the rightmost point (right figure) Mine Mine Mine Yours Yours Yours 25 Pictorial Games: Broken Scales in Column Charts Effect similar to non-zero origins in line charts Break the scale in the middle Can be used to amplifies negligible performance differences Two entirely different messages about the same data are possible 0 26
14 Special Charts: Gantt Chart Motivation: Optimization of resource utilization Good utilization requires a mix of jobs using different resources with a significant overlap. Overlapping jobs need to be independent. The overlapping utilization of resources can be depicted in Gantt charts 27 Source: Special Charts: Timeline Chart Later! 28
15 Special Charts: Kiviat Graphs Circular graph Helps (system) managers to quickly recognize performance problems Metrics are plotted along radial lines Popular version with HB (higher is better) and LB (lower is better) metrics 3D variant with z-axis representing the time: Time tunnel 29 Special Charts: 3D Kiviat Graphs Standard Kiviat animation: Fly through a tunnel Problem: User sees only on time stamp at a time Remove animation and introduce a third axis Gives a global perspective of the performance data Obscures more detailed information Introduce transparency and display just a single 2D Kiviat slice 30
16 Special Charts: Treemaps Initial motivation: Fin largest files and users who put them there Display hierarchical data as groups of rectangles Can be arranged, sized and colored to graphically reveal underlying patterns Aspect ratio vs. item placement or order 31 Special Charts: Treemaps 32
17 Vampir: Technical Components VampirTrace Vampir File (.otf/.vtf) Vampir Trace 1 Trace 2 Trace 3 Trace P Worker 1 Worker 2 Worker m Vampir Server Master 1. Trace generator 2. Trace file(s) 3. Alignment and conversion tools 4. Viewer and analyzer 5. Parallel server engine 33 Vampir Displays Global Timeline Process Timeline Summary Chart Summary Timeline Counter Timeline Process Profile Call Tree Message Statistics Filters 34
18 Vampir Displays - Global Timeline show temporal behavior for processes functions resp. function groups messages collective ops I/O activities context information on click zoomable control actual time interval for other displays 35 Vampir Displays - Global Timeline 36
19 Vampir Displays - Global Timeline with Thumbnail Global Timeline with Thumbnail View 37 Vampir Displays - Global Timeline with Thumbnail Zoomed 38
20 Vampir Displays - Global Timeline with Thumbnail Further Zoomed 39 Vampir Displays - Global Timeline with Thumbnail Even further Zoomed 40
21 Vampir Displays - Process Timeline timeline for single processes unfolded call stack in vertical dimension shows: functions/function groups messages, collective communication I/O activities performance counter values zoomable in time context display 41 Vampir Displays - Process Timeline 42
22 Vampir Displays Aligned Process Timelines 43 Vampir Displays - Process Timeline Zoom I 44
23 Vampir Displays - Process Timeline Zoom II 45 Vampir Displays - Process Timeline Zoom III 46
24 Vampir Displays - Process Timeline with Context 47 Vampir Displays - Process Timeline with FLOPS 48
25 Vampir Displays - Process Timeline with Two Counters 49 Vampir Displays - Process Timeline Zoomed 50
26 Vampir Displays - Counter Timeline 51 Vampir Displays - Summary Chart statistical overview over functions resp. groups of functions as bar chart, pie chart or table global (all processes) or local (single process) exclusive/inclusive time, occurrences absolute or logarithmic scale absolute values or percentages zoomable respect the current time interval (according to timeline) 52
27 Vampir Displays - Summary Chart Grouped / Comprehensive Function Statistics 53 Vampir Displays - Summary Chart Grouped / Comprehensive Function Statistics 54
28 Vampir Displays - Summary Chart Alternative Representation 55 Vampir Displays - Summary Timeline Summary Chart over time Similar functionality Identify load balancing issues 56
29 Vampir Displays - Summary Timeline Also supports zooming Provides a global view on load balancing and a program s iterations By far more scalable than normal timeline display 57 Vampir Displays - Process Profile Profile for function/group of functions per process Similar to results of regular profiling But available for arbitrary time intervals 58
30 Vampir Displays - Process Profile (MPI Function Group) 59 Vampir Displays - Call Tree Shows function invocation hierarchy Provide callers & callees Number of function invocations Minimum and maximum runtime Folding Searching 60
31 Vampir Displays - Call Tree 61 Vampir Displays - Message Statistics sender-receiver matrix zoomable, for > 1000 processes show message properties: Length, rate duration count optionally as: min, max, avg, sum (message length histogram) 62
32 Vampir Displays - Message Statistics Message Statistics 63 Vampir Displays - Message Statistics Zoomed Message Statistics 64
33 Vampir Displays - Global Filters ignore certain items globally processes/threads or groups of them messages by communicator or by tag collective operations by communicator or by type I/O events by communicator by file by read/write access 65 Vampir Displays - Global Filters Event Filter Dialog 66
34 Vampir Displays - Global Filters Process Filter Dialog 67 Center for Information Services and High Performance Computing (ZIH) Thank You! Nöthnitzer Straße 46 Raum 1026 Tel Holger Brunst (holger.brunst@tu-dresden.de) Matthias S. Mueller (matthias.mueller@tu-dresden.de)
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