wiki:HomogeneousRedundancy

Version 11 (modified by davea, 16 years ago) (diff)

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Dealing with numerical discrepancies

Most numerical applications produce different outcomes for a given workunit depending on the machine architecture, operating system, compiler, and compiler flags. For some applications these discrepancies produce only small differences in the final output, and results can be validated using a 'fuzzy comparison' function that allows for deviations of a few percent.

Other applications are 'divergent' in the sense that small numerical differences lead to unpredictably large differences in the final output. For such applications it may be difficult to distinguish between results that are correct but differ because of numerical discrepancies, and results that are erroneous. The 'fuzzy comparison' approach does not work for such applications.

Eliminating discrepancies

One approach is to eliminate numerical discrepancies. Some notes on how to do this for Fortran programs are given in a paper, Massive Tracking on Heterogeneous Platforms and in an earlier text document, both courtesy of Eric McIntosh from CERN.

Homogeneous redundancy

BOINC provides a feature called homogeneous redundancy (HR) to handle divergent applications. HR divides hosts into 'numerical equivalence classes': two hosts are in the same class if they return identical results for your applications. The BOINC scheduler will send results for a given workunit only to hosts in the same class; this lets you use strict equality to compare redundant results.

You can enable HR for a project by including the line

<homogeneous_redundancy>N</homogeneous_redundancy>

in the config.xml file, where N is the "HR type" to use (see below).

Alternatively, you can enable HR for a single application by setting the homogeneous_redundancy field in its database record to the HR type for use with that application.

An "HR type" is a host classification. Currently the following HR types are defined:

0
No homogeneous redundancy (all hosts are numerically equivalent)
1
A fine-grained classification with 80 classes (4 OS and 20 CPU types).
2
A coarse-grained classification in which there are 4 classes: Windows, Linux, Mac-PPC and Mac-Intel.

The proper classification depends on your application, and how it's compiled (compiler, compiler options, math libraries) on the various platforms. For example, WCG reports that the following gcc options (on Linux) cause their apps to produce identical results on all processor types:

-mieee-fp -O3 -fno-rtti -ffor-scope -DNDEBUG

This allows them to use HR type 2.

There are two ways to find what HR type is needed for a given application. The bottom-up approach is to use a fine classification, and (by manually examining result files) identify classes that can be merged. The top-down approach is to use a coarse classification (e.g., 0) and (by analyzing the hosts involved in validation failures) identify host types that much go in separate classes.

You can modify the pre-defined HR types, or add your own, by editing the file sched/hr.C.

Taking a census of hosts

If you use HR, it's important to tell the feeder roughly what fraction of hosts belong to each HR class; this allows it to allocate space in its shared-memory work array in proportion to this fraction. This information is passed to the feeder in a file hr_info.txt in your project's root directory. You can generate this file by running sched/census. Run this as a periodic task to track changes in your host population; example config.xml entry:

<task>
   <cmd>census</cmd>
   <period>1 day</period>
</task>

The BOINC distribution includes a file sched/sample_hr_info.txt containing host-distribution data from a large project. You can use this e.g., during the period when your project is starting up and doesn't have a lot of hosts yet.

If you send the feeder a SIGUSR1 signal, it will write a summary of shared-memory contents, and allocations among HR classes, to its log file. This may be useful in debugging problems related to HR.