Changes between Version 2 and Version 3 of ValidationSummary

Timestamp:

Jun 10, 2008, 8:36:47 AM (16 years ago)

Author:

davea

Comment:

--

ValidationSummary

@@ -12 +12 @@
  * Some volunteers may return correct results but falsify the CPU time.
 
+BOINC offers several mechanisms for validating results and credit.
+However, there is no "one size fits all" solution.
+The choice depends on your requirements,
+and on the nature of your applications
+(you can use different mechanisms for different applications).
+
+For each of your applications,
+you must supply two server-side programs:
+ * A "validator", which decides whether results are correct;
+ * An "assimilator", which handles validated results
+
+PICTURE: different validator/assim for different apps
+
+BOINC provides examples of each of these,
+as well as a framework that makes it easy to develop your own.
+
+== No replication ==
+
+The first option is to not use replication.
+Each job gets done once.
+The validator examines single results.
+
+This approach is useful if you have some way (application-specific)
+of detecting wrong results with high probability.
+
+The credit question remains.
+Some possibilities:
+
+ * Grant fixed credit (feasible if your jobs are uniform).
+ * Put a cap on granted credit (this allows cheating).
+ * If claimed credit exceeds a threshold, replicate the job.
+
+== Replication ==
+
+BOINC supports replication:
+each job gets done on N different hosts,
+and a result is considered valid if a strict majority of hosts return it.
+
+Replication also provides a good solution to credit cheating, even for non-uniform apps:
+grant the average claimed credit, throwing out the low and high first
+(if N=2, grant the minimum).
+
+One problem with replication is that there are discrepancies
+in the way different computers do floating point math.
+This makes it hard to determine when two results "agree";
+two different results may be equally correct.
+
+There are several different ways of dealing with this problem.
+
+=== Eliminate discrepancies ===
+
+By selecting the right compiler, compiler options, and math libraries,
+it may be possible to eliminate numerical discrepancies.
+This lets you do bitwise comparison of results.
+However, it is difficult and generally reduces the performance of your application.
+
+=== Fuzzy comparison ===
+
+If your application is numerically stable
+(i.e., small discrepancies lead to small differences in the result)
+you can write a "fuzzy comparison function" for the validator that
+considers two results as equivalent if they agree within some tolerance.
+
+=== Homogeneous replication ===
 There are
-                purposes: correct results, correct credit
-                no redundancy
-                        (still can validate)
                         may bound credit
                 redundancy