(2017/12/14 : Updated the final AMI to reflect changes requested. This should not affect people unless they are using GPUs or extra libraries.
(2017/12/04 : Clarifying (again) the stress penalty, to make the documentation match the code.
(2017/11/28 : See the end of the readme for some additional notes)
This coursework is due on Fri 15th Dec at 22:00.
The goal of this coursework is to take an existing library called TestU01, and parallelise, optimise, or otherwise accelerate it.
TestU01 is used for testing random number generators (RNG), and tries to empirically look for statistical flaws in the output of an RNG that are not likely to occur by chance. As RNGs have improved, the number of samples needed to show non-randomness has also increased, so these days we might need to look at 2^38 or more random numbers before seeing failures, and apply many different types of statistical tests. But because the random numbers are often used in massively parallel compute simulations, the number of random numbers consumed in one simulation is growing to 2^48 or more, so it is important to look for flaws in even larger samples.
There is substantial documentation for TestU01 in the included
pdfs. It covers the main APIs, design intent, and also the details
of the statistics. It is up to you to decide how deep you want
to go. Originally TestU01 was single-threaded C, but it has been
converted to C++ and made thread-safe. The only remaining unsafe
part I'm aware of is the unif01_Gen struct - you cannot safely use
the same unif01_Gen class from two different tasks or threads
at the same time. You can use two different instances of
unif01_Gen at the same time, but they must be seperate.
Your goal is to optimise the TestU01 library with three use-cases in mind (or two for a pair):
Certification: making sure that an RNG can pass a relatively slow standardised test-suite, in this case the Crush benchmark. The metric is simply the execution time (latency).
Search: evaluating many different RNG instances in order to search for good RNG parameters. The metric is how many times the SmallCrush benchmark can be executed per second, which is measured as the number of complete test results printed to stdout, divided by the amount of time the program is allowed to run before the user terminates it (this will be between 10 and 100 seconds).
Stress testing : Trying to test as large a sample
as possible within a time budget, using the benchmark Rabbit. Given a
time-budget t seconds of between 2 and 180 seconds
(passed as a command-line parameter), your program should
respond within 0.5 sec with a bid size n printed to stdout, and then
proceed to apply the test. If the total measured execution time of the
program is g seconds, then the metric is log2(n) * ( (g/t<1) ? 1.0 : exp(-g/t) ).
The idea is that you should achieve the largest n possible within the
time budget, but that you are penalised for exceeding that budget.
See drivers/driver_stress.cpp as a model
of the logic (though feel free to change the internals), and also see
the discussion [here]() for an explanation of the intent.
As part of the input to the use-cases there is also an RNG source
called the "workload" (see include/workload.hpp). This is code
supplied by the user, and not under your control. So the inputs to
an application are:
Your (potentially customised) TestU01 library
Your (potentially customised) driver program
An externally sourced workload containing the RNG(s)
There is an example workload in the workloads directory called
workload_std.cpp, which exposes two random number generators
from the C++ standard library. The default makefile knows
how to build bin/${use-case}_${workload} from drivers/driver_${use-case}.cpp
and workload/workload_${workload}.cpp.
Stress testing for std workload with a random deadline:
$ make bin/stress_std
$ bin/stress_stdor with a chosen deadline of 10 seconds:
$ make bin/stress_std
$ bin/stress_std 10.0Search for std workload:
$ make bin/search_std
$ bin/search_stdCertification for std workload:
$ make bin/certify_std
$ bin/certify_stdCompilation for assesment will consist of copying a workload
into workloads, then running make bin/{use-case}_{workload}.
Execution will start from the root directory of the repository,
as in the examples above.
Correctness for statistical testing is quite interesting, as it doesn't
matter which part of an RNG's output stream is tested, as long as each
test only looks at some contiguous and in-order part of the stream. So
if one test consumes n random number, then it could consume x_1,x_2,...x_{n},
or x_{n},x_{n+1},...,x_{2n} - statistical quality should be invariant
to where you look in the stream, However, it does depend on the order
in which you look, so you can't test x_1,x_4,x_10,x_3,... as randomness
also depends on the ordering of the samples.
One approach to correctness is to return bit-exact answers compared to the sequential version, which makes verification easier. However, it also limits flexibility and is a bit unrealistic. Instead, as long as your tests still give the same statistical answer, then it is acceptable. Each test returns a p-value, and they should behave in a particular way statistically - if the RNG is passing the test, then they look like uniform numbers in U(0,1), otherwise they are heavily biased towards 0 or 1. I'll be testing your implementations on different workloads, and have a very good idea of what the distribution of p-values should look like for each (i.e. I can tune workloads to particular known p-value distributions), so as long as your tests are statistically plausible then they are correct.
This gives you the freedom to play around with algorithms and floating-point issues, but if you want to stick to bit-exact answers that should work too.
As far as I'm aware I've eliminated all thread unsafe conditions from TestU01, but there may still be things lurking. I've done soak tests on multi-core machines where I run the benchmarks repeatedly in parallel with themselves, but experience suggests something else is likely to turn up. To encourage people to share (as this is particularly difficult to deal with for those weaker on programming), I'm willing to award up to a 2% bonus (for this coursework) to groups who find something that is thread unsafe and can point to the problem area (and ideally a fix). I'm not too worried about latent bugs, or problems with IO paths that aren't exercised (I'm aware that the standard report writing is interleaved), but anything that causes genuine race conditions or thread-safety related crashes crashes is of particular interest.
General bug-reports and bug-fixes are still very much welcome, but I particularly want to reward any thread-unsafe parts as they tend to be difficult to debug and affect many people, and generally demonstrate the most expertise on the part of those who can find and fix them.
The target environment is either a g3.4xlarge (note that this is
newer than in previous courseworks), or a c4.8xlarge. You
should indicate in group.md which one you want to use.
The AMI used will be HPCE-2017-GPU-Image-v2. This is identical
to HPCE-2017-GPU-Image except for:
The marks for the coursework are split up into three parts:
Performance (60%) : How well it does in the two (pairs) or three (triples) use-cases. As before, each pair should nominate two uses-cases.
Group analysis (20%) : Each group needs to provide two (pairs) or three (triples)
suggestions for improvement - see the attached group.md.
Individual analysis (20%) : Each individual should separately submit a
document reflection.md via blackboard.
The blackboard submission of hashes this year has not got smoothly for some reason; I think a bunch of people forgot. The main purpose for this is to provide some safety and a paper trail for both students and staff, so I strongly encourage you to submit a group hash to blackboard. It's essentially providing proof that a particular submission existed at a point in time, and also makes it much more likely that the version committed is compileable.
You may be confused about how to get started. Some suggestions are:
Compile and run the programs.
Get some initial high-level profiling, e.g. using perf or gdb
Try to get a feel for the scaling properties of each use-case.
Try to work out the important tasks in the program, and the flows of data between different tasks.
Brain-storm some possible strategies for improvement, and try to identify the effort versus improvement of each.
Allocate strategies to people and get started.
A few people have been getting a bit frustrated, or worried that they can't immediately see what to do (while I can see that others are happily ticking along with parallelising, so it is not a uniform experience).
There is no new information to come (as we have no more lectures), and the only big hint is in the existing readme and the way the coursework is setup (it requires thinking about the problem at a high-level).
It is a difficult task, and part of the approach is about being realistic. So some generic advice/observations:
Don't start at the lowest-level. Initial profiling is not supposed to help you optimise, it is supposed to help you understand where the hot-spots are.
You shouldn't be expecting huge easy speed-ups, as that doesn't happen in most applications. There are a number of initial opportunities that can be identified (some of which are very effective), but each one requires changing the code in a few places, and thinking carefully about whether it is safe.
There is no correct answer or single point of attack. I have identified multiple sources of speed-up in each use-case (and exploited them), so I know they are there. They also align well with the lectures and concepts taught in the course, but you have to look for them.
Some use-cases are intrinsically harder than others, due to the types of potential parallelism and optimisations at play. Some can be made to scale linearly, some can't, but they can all be made faster with parallelism, and faster still with restructuring and optimisation.
You need to decide an appropriate level of time investment, then stop. The EEE guidelines for a 100% coursework course are an upper-bound of 150 hours total, so about 130 hours max on the coursework parts. Hopefully most people spend much less than this - personally I would probably invest at most 20 hours per-person of active time in CW6.
Time invested is often more effective when it is spread around. One of the use-cases takes significant time to run (particularly before initial parallelisation), so you should be thinking of running experiments while you're doing something else, then coming back to the results later on. It is your active time that is important, so only when you are thinking and doing - if you are staring at a screen watching results tick past (or waiting for results) for more than 1 min, then you might rethink how you are working. It is sometimes better to decide to wait 1 hour and get rich results, rather than limit yourself to shallow results limited by how long you are willing to wait for things to run.
The actual performance part of the the coursework is only worth 60% of the marks, and that is across all three (or two) use-cases. If you can't get much speed-up it is not a disaster (similar to not being able to answer 20% of an exam, when you've already completed 80% of the exam's questions, and know that you aced the first 40%).
Ultimately, it's a masters course where everybody has already got 40% built-in - parts of the assesment need to actually test skill and insight, and whether people can generalise the approaches and solutions in the course to new problems.