![[DBPP]](pictures/asm_color_tiny.gif)
![[Search]](pictures/search_motif.gif)
Next, we describe a number of both public-domain and commercial performance tools, explaining how each is used to collect and display performance data. While the tools exhibit important differences, there are also many similarities, and frequently our choice of tool will be driven more by availability than by the features provided.
Paragraph is a portable trace analysis and visualization package developed at Oak Ridge National Laboratory for message-passing programs. It was originally developed to analyze traces generated by a message-passing library called the Portable Instrumented Communication Library (PICL) but can in principle be used to examine any trace that complies to its format. Like many message-passing systems, PICL can be instructed to generate execution traces automatically, without programmer intervention.
Paragraph is an interactive tool. Having specified a trace file, the user instructs Paragraph to construct various displays concerning processor utilization, communication, and the like. The trace files consumed by Paragraph include, by default, time-stamped events for every communication operation performed by a parallel program. Paragraph performs on-the-fly data reduction to generate the required images. Users also can record events that log the start and end of user-defined ``tasks.''
Paragraph's processor utilization displays allow the user to distinguish time spent computing, communicating, and idling. Communication time represents time spent in system communication routines, while idle time represents time spent waiting for messages. These displays can be used to identify load imbalances and code components that suffer from excessive communication and idle time costs. Some of these displays are shown in Plate 8,
which shows a Gantt chart (top part) and a space time diagram (bottom part) for a parallel climate model executing on 64 Intel DELTA processors. In the space-time diagram, the color of the lines representing communications indicates the size of the message being transferred. The climate model is a complex program with multiple phases. Initially, only processor 0 is active. Subsequently, the model alternates between computation and communication phases. Some of the communication phases involve substantial idle time, which should be the subject of further investigation.
Communication displays can be used both to obtain more detailed information on communication volumes and communication patterns and to study causal relationships, for example between communication patterns and idle time. Plate 10
shows some of these displays, applied here to the trace data set of Plate 8.
The communication matrix on the left and the circle on the right both show instantaneous communication patterns. The colors in the communication matrix indicate communication volume, as defined by the scale above the matrix. Most matrix entries are on the diagonal, which indicates mostly nearest-neighbor communication. Another display in the top right presents cumulative data on processor utilization.
Plate 10 is not available in the online version.
A disadvantage of Paragraph is that the relationship between performance data and program source is not always clear. This problem can be overcome in part by explicitly logging events that record the start and end of ``tasks'' corresponding to different phases of a program's execution. Paragraph provides task Gantt and task histogram displays to examine this information.
Of the portable tools described here, Paragraph is probably the simplest to install and use. Because it operates on automatically generated traces, it can be used with little programmer intervention. Paragraph displays are particularly intuitive, although the inability to scroll within display windows can be frustrating.
Upshot is a trace analysis and visualization package developed at Argonne National Laboratory for message-passing programs. It can be used to analyze traces from a variety of message-passing systems: in particular, trace events can be generated automatically by using an instrumented version of MPI. Alternatively, the programmer can insert event logging calls manually.
Upshot's display tools are designed for the visualization and analysis of state data derived from logged events. A state is defined by a starting and ending event. (For example, an instrumented collective communication routine can generate two separate events on each processor to indicate when the processor entered and exited the routine.) The Upshot Gantt chart display shows the state of each processor as a function of time. States can be nested, thereby allowing multiple levels of detail to be captured in a single display. States can be defined either in an input file or interactively during visualization. A histogramming facility allows the use of histograms to summarize information about state duration (Plate 11).
(GIF 24137 bytes.) Plate 11: Gantt chart, state duration histogram, and instantaneous state diagram for a search problem running on 16 processors, generated using Upshot. Image courtesy of E. Lusk.
illustrates the use of nested states within Upshot. This is a trace generated from a computational chemistry code that alternates between Fock matrix construction (Section 2.8) and matrix diagonalization, with the former taking most of the time. Each Fock matrix construction operation (blue) involves multiple integral computations (green). A substantial load imbalance is apparent---some processors complete their final set of integrals much later than do others. The display makes it apparent why this load imbalance occurs. Integrals are being allocated in a demand-driven fashion by a central scheduler to ensure equitable distribution of work; however, smaller integrals are being allocated before larger ones. Reversing the allocation order improves performance.
Plate 12 is not available in the online version.
Upshot provides fewer displays than does Paragraph, but has some nice features. The ability to scroll and zoom its displays is particularly useful.
The Pablo system developed at the University of Illinois is the most ambitious (and complex) of the performance tools described here. It provides a variety of mechanisms for collecting, transforming, and visualizing data and is designed to be extensible, so that the programmer can incorporate new data formats, data collection mechanisms, data reduction modules, and displays. Predefined and user-defined data reduction modules and displays can be combined in a mix-and-match fashion by using a graphical editor. Pablo is as much a performance tool toolkit as it is a performance tool proper and has been used to develop performance tools for both message-passing and data-parallel programs.
A source code instrumentation interface facilitates the insertion of user-specified instrumentation into programs. In addition, Pablo calls can be incorporated into communication libraries or compilers to generate trace files automatically. When logging an event, Pablo can be requested to invoke a user-defined event handler that may perform on-the-fly data reduction. For example, a user-defined handler can compute communication statistics rather than logging every message or can combine procedure entry and exit events to determine procedure execution times. This very general mechanism provides great flexibility. A disadvantage is that the overhead associated with logging an event is greater than in other, less general systems.
A novel feature of Pablo is its support for automatic throttling of event data generation. The user can specify a threshold data rate for each type of event. If events are generated at a greater rate, event recording is disabled or replaced by periodic logging of event counts, thereby enabling a variety of events to be logged without the danger that one will unexpectedly swamp the system.
Pablo provides a variety of data reduction and display modules that can be plugged together to form specialized data analysis and visualization networks. For example, most displays provided by Paragraph can be constructed using Pablo modules. This feature is illustrated in Plate 13,
which shows a variety of Paragraph-like displays and the Pablo network used to generate them. As noted earlier, Pablo uses its own SDDF.
(GIF 67844 bytes; RGB 635350 bytes.) Plate 13: Pablo display of performance data collected from a numerical solver. Image courtesy of D.~Reed.
An interesting feature of the Pablo environment is its support for novel ``display'' technologies, such as sound and immersive virtual environments. Sound appears to be particularly effective for alerting the user to unusual events, while immersive virtual environments can be used to display higher-dimensional data, as illustrated in Plate 14.
In this plate, each cube represents a different performance metric, and the spheres within the cubes represent processors moving within a three-dimensional metric space. While both approaches are still experimental at present, they are suggestive of future directions.
(GIF 184245 bytes; RGB 622138 bytes.) Plate 14: Pablo virtual reality display of performance data. Image courtesy of D. Reed.
The Gauge performance tool developed at the California Institute of Technology is distinguished by its focus on profiles and counters rather than execution traces. The Gauge display tool allows the user to examine a multidimensional performance data set in a variety of ways, collapsing along different dimensions and computing various higher-order statistics. For example, a three-dimensional view of an execution profile uses color to indicate execution time per processor and per routine; corresponding two-dimensional displays provide histograms for time per routine summed over all processors or for time per processor for all routines. Idle time is also measured on a per-processor basis and associated with program components by determining which task is enabled by arrival of a message. Some of these displays are illustrated in Plate 7.
The ParAide system developed by Intel's Supercomputer Systems Division is specialized for the Paragon parallel computer. It incorporates a variety of different tools. Modified versions of the standard Unix prof and gprof tools provide profiling on a per-node basis. An enhanced version of Paragraph provides various data reduction and display mechanisms. The System Performance Visualization system uses displays specialized for the Paragon's two-dimensional mesh architecture to show data collected by hardware performance monitors. These provide detailed low-level information regarding the utilization of the processor, communication network, and memory bus. This fine level of detail is made possible by hardware and operating system support in the Paragon computer.
The IBM AIX Parallel Environment is specialized for IBM computers, in particular the SP multicomputer. It incorporates a variety of different tools. A variant of the standard Unix prof and gprof commands can be used to generate and process multiple profile files, one per task involved in a computation. The Visualization Tool (VT) can be used to display a variety of different trace data. Three types of trace data are supported, as follows:
VT displays are similar to those provided by Paragraph in many respects, but they give the programmer greater flexibility in how data are displayed and can deal with a wider range of data. Plate 15
shows one display, in this case a space-time diagram.
The Automated Instrumentation and Monitoring System (AIMS) developed at the NASA Ames Research Center provides both instrumentation tools and a variety of trace visualization mechanisms for message-passing programs. Users can either specify trace events manually or request AIMS to log communication events and procedure calls automatically. The resulting traces can be visualized by using the AIMS View Kernel (VK), Pablo, or Paragraph. A strength of AIMS is its tight integration with a source code browser that allows the user both to mark code blocks for tracing and to relate communication events with source code. For example, the user can click on a line representing a communication in a space-time diagram to identify the corresponding communication operation in the source code. AIMS also provides statistical analysis functions that can be used to determine average resource utilization and message latencies.
We conclude this section by noting that while general-purpose tools have the advantage of being easy to use, custom performance tools can also be valuable, particularly in understanding the performance of a complex parallel program. Extensible tools such as Pablo can be useful in this regard. So can text manipulation systems such as awk and PERL, statistical packages such as Mathematica and Matlab, and general-purpose graphics packages such as AVS.
As an example of this approach, Plate 5
shows an image generated by a tool developed specifically to help understand load imbalances in a parallel climate model. This tool collects timing data by using interval timers and counters inserted manually into the parallel climate model. The data are postprocessed to compensate for timer overhead and are then displayed by using a general-purpose graphics package. Sequences of such images provide insights into how computational load varies over time, and have motivated the design of load-balancing algorithms.
© Copyright 1995 by Ian Foster