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+# Credit options
+
+"Credit" is a number associated with completed jobs,
+reflecting how much floating-point computation was (or could have been) done.
+For CPU applications the basic formula is:
+
+1 unit of credit = 1/200 day runtime on a CPU whose Whetstone benchmark is 1 GFLOPS.
+
+Whetstone measures peak performance, and applications that do a lot of memory or disk access get lower FLOPS.
+So credit measures peak, not actual, FLOPs.
+
+Credit is used for two purposes:
+
+ 1. For users, to see their rate of progress,
+  to compete with other users or teams,
+  and to compare the performance of hosts.
+
+ 2. To get an estimate of the peak performance available to a particular project,
+  or of the volunteer host pool as a whole.
+
+For 2) we care only about averages.
+For 1) we also care about parity between similar jobs;
+users get upset if someone else gets a lot more credit for a similar job.
+
+BOINC provides 4 ways of determining credit.
+The choice (per app) depends on the properties of the app:
+
+* If you can estimate a job's FLOPs in advance, use **pre-assigned** credit.
+
+* Else if you can estimate a job's FLOPs after if completes, use **post-assigned** credit.
+
+* Else if the app has only CPU versions, use **runtime credit**.
+
+* Else use **adaptive credit**.
+
+# Pre-assigned credit
+
+You can use this if the amount of computation done by each job is known in advance,
+e.g. if all jobs do the same computation.
+Measure the runtime on a machine with known Whetstone benchmarks.
+Pick a machine with enough RAM that you're not paging.
+The credit is then
+
+(runtime in days)*benchmark*ncpus*200
+
+ncpus is the number of CPUs used by the app version; use a sequential version if possible.
+
+You can also use it if the runtime is a linear function of
+some job attribute (e.g. input file size) that's known in advance.
+
+To specify:
+* use the --credit argument to the create_work cmdline program
+* if using the C++ API, assign wu.canonical_credit in the first argument.
+
+You must run the app's validator with the **--credit_from_wu** option.
+
+TODO: add to remote job submissions RPCs if anyone wants.
+
+# Post-assigned credit
+
+Use this if you can estimate the FLOPs done by a completed job,
+based on the contents of its output files or stderr.
+For example, if your app has an outer loop,
+and you can measure (as above) the credit C due for each iteration,
+the job credit is C times the number of iterations performed.
+
+To use this:
+* In your validator, have the init_result() function set result.claimed_credit.
+* Run the validator with **--post_assigned_credit**.
+
+A job's granted credit is the claimed credit of its canonical instance.
+
+# Runtime-based credit
+
+Use this if the app has only CPU app versions.
+The "claimed credit" for a job instance is runtime*ncpus*peak_flops,
+where peak_flops is the host's Whetstone benchmark.
+The job's granted credit is the average of the instance claimed credits.
+
+To use this: pass the **--credit_from_runtime** option to the app's validator.
+You must also supply **--max_granted_credit**.
+
+Runtime-based credit can't be used if the app has GPU versions
+because efficiency can vary by orders of magnitude between CPU and GPU versions.
+
+Runtime-based credit is limited by max_granted_credit, but is otherwise not cheat-proof.
+
+# Runtime-based credit via trickle messages
+
+If you have very long-running jobs (a week or more) you may want to
+grant credit incrementally.
+To do so:
+
+* Have your application periodically send [trickle-up messages](TrickleApi)
+  with variety **runtime** and content
+```
+<runtime>X</runtime>
+```
+  where X is the runtime since the last trickle message.
+
+* Run the **trickle_credit** daemon as follows:
+```
+trickle_credit --variety runtime --max_runtime Y
+```
+where Y is the limit on runtime
+(typically the period of the trickle messages).
+
+* Run your validator for the app with the **--no_credit** option
+
+The **trickle_credit** daemon grants credit in proportion to (runtime * CPU FLOPS),
+hence this approach should be used only for applications with only single-CPU versions.
+
+This approach is not device-neutral because hosts with the same peak FLOPS
+may have different actual FLOPS for the app version.
+
+# Adaptive credit
+
+Use this if you have GPU apps, and are unable to estimate FLOPs even after job completion.
+This method maintains performance statistics on a (host, app version) level,
+and uses these to normalize credit between CPU and GPU versions.
+See [CreditNew].
+
+To use: this is the default.
+
+If you use this, the adaptation will happen faster if you provide
+values for workunit fp_ops_est that are correlated with the actual FLOPs.
+Use a constant value if you're not sure.
+
+# Credit averaging
+
+If you use replication, runtime-based and adaptive credit can produce
+different "claimed credit" for each job instance.
+The validator code averages these in tricky way I don't quite understand
+(Kevin invented it).
+It does not take the minimum.
+We should probably provide this as an option,
+to make runtime-based credit more cheat-proof.
+However, this won't work if you use adaptive replication,
+where many jobs have only one instance.