I want to check what is the definition of «iterative» in expansion regions in activity diagrams. For me personally this was never a question because I understand it as letting me do a For loop, e.g.,
For i=1 to 10
Do-Something // So it does it 10 times
End For
However, while I was presenting my UML diagram to an audience, an engineer team leader (not a UML maven) objected against the term ‘iterative’, because he understood ‘iterative’ to mean an 'iterative process' such that each step improves a result. I am also aware of this definition, but I assume the UML definition is not that, but rather means a simple For-Loop.
Please confirm that the UML definition of «iterative» and iteration is like a simple For-loop. Or otherwise, if so.
No, it has a different meaning. UML 2.5 states in p. 480:
The mode of an ExpansionRegion controls how its expansion executions proceed.
If the value is iterative, the expansion executions must occur in an iterative sequence, with one completing before another can begin. The first expansion execution begins immediately when the ExpansionRegion starts executing, with subsequent executions starting when the previous execution is completed. If the input collections are ordered, then the expansion executions are sequenced in the order induced by the input collection. Otherwise, the order of the expansion executions is not defined.
Other values for this keyword are parallel and stream. You can guess that behavior defined in a parallel region can be executed in parallel. stream is a bit more complicated and you might read on that page in the UML spec.
The for-loop itself comes from the input collection you pass to the region. This can be processed in either of the above ways.
tl;dr
So rather than a for loop the keyword «iterative» for the region tells that it's behavior may not be handeled in parallel.
Ahhh, semantics...
First a disclaimer - I am not a native English speaker. Yet my believe both my level of English and IT experience are sufficient to answer this question.
Let's have a look at the dictionary definition of iterative first:
iterative adjective
/ˈɪtərətɪv/
/ˈɪtəreɪtɪv/, /ˈɪtərətɪv/
(of a process) that involves repeating a process or set of instructions again and again, each time applying it to the result of the previous stage
We used an iterative process of refinement and modification.
an iterative procedure/method/approach
The highlight with a script font is mine.
Of course this is a pure word definition, not in the context of software development.
In real life a process can quite easily be considered repetitive but in itself not really iterative. Imagine an assembly line in a mass production factory. On one of the positions a particular screw/set of screws is applied to join two or more elements. For every next run, identical set of elements the same type and number of screws is applied. There is a virtually endless stream of similar part sets, each set consisting of the same type of parts as previously and requiring the same kind of connection. From the position perspective joining the elements is a repetitive process but it is not iterative, as each join is applied to a different set of elements - it does not apply to those already joined.
If you think of a code, it's somewhat different though. When applying a loop, almost always you have some sort of a resulting set impacted by it and one can argue that with every loop step that resulting set is being further changed, meaning the next loop step is applied on the result of the previous step. From this perspective almost every loop is iterative.
On the other hand, you can have a loop like that:
loop
wait 10
while buffer is empty
read buffer
You can clearly say it is a loop and nothing is being changed. All the code does is waiting for a buffer to fill. So it is not iterative.
For UML specifically though the precise meaning is included in qwerty_so's answer so I will not repeat it here.
Related
Why subpass contents can't at the same time support VK_SUBPASS_CONTENTS_INLINE and VK_SUBPASS_CONTENTS_SECONDARY_COMMAND_BUFFERS? I want to use gbuffer and second command buffers to render scene.
TL;DR: Because the specification says so. Put your inline commands into one or more separate secondary command buffers.
Long version:
Why subpass contents can't at the same time support VK_SUBPASS_CONTENTS_INLINE and VK_SUBPASS_CONTENTS_SECONDARY_COMMAND_BUFFERS?
If you're asking why you literally can't combine them, it's because they're not bit flags, but a sequence. Bit flags, like VkBufferUsageFlagBits will typically have values that each represent a single bit in a 32 bit value.
Sequences like VkSubpassContents have values that start at 0 and increment by 1 each time (although extension provided values will often jump ahead).
Since VK_SUBPASS_CONTENTS_INLINE is literally 0, there's no way to combine it with VK_SUBPASS_CONTENTS_SECONDARY_COMMAND_BUFFERS, which is literally 1.
If you're asking why VkSubpassContents is a sequence and not a bit flag, that's just the way the specification is. It might seem like having a subpass include both inline commands and secondary buffers might be trivial, but it probably only seems that way to people using the API, as opposed to be people who have to implement the backend. Likely it either created some potential ambiguity, or would have made some threading edge case a nightmare to implement, or something similar.
I want to use gbuffer and second command buffers to render scene.
As Nicol points out in his comments, there's nothing stopping you from doing that. Whatever inline commands you're trying to use along with your secondary command buffers, you can just put into another secondary buffer. If this is somehow problematic because you're interleaving lots of inline statements with where you want to execute your secondary buffers, well that sounds more like a design problem, like maybe you're trying to execute work in a subpass that doesn't belong there.
I'm working on a streaming rules engine, and some of my customers have a few hundred rules they'd like to evaluate on every event that arrives at the system. The rules are pure (i.e. non-side-effecting) Boolean expressions, and they can be nested arbitrarily deeply.
Customers are creating, updating and deleting rules at runtime, and I need to detect and adapt to the population of rules dynamically. At the moment, the expression evaluation uses an interpreter over the internal AST, and I haven't started thinking about codegen yet.
As always, some of the predicates in the tree are MUCH cheaper to evaluate than others, and I've been looking for an algorithm or data structure that makes it easier to find the predicates that are cheap, and that are validly interpretable as controlling the entire expression. My mental headline for this pattern is "ANDs all the way to the root", i.e. any predicate for which all ancestors are ANDs can be interpreted as controlling.
Despite several days of literature search, reading about ROBDDs, CNF, DNF, etc., I haven't been able to close the loop from what might be common practice in the industry to my particular use case. One thing I've found that seems related is Analysis and optimization for boolean expression indexing
but it's not clear how I could apply it without implementing the BE-Tree data structure myself, as there doesn't seem to be an open source implementation.
I keep half-jokingly mentioning to my team that we're going to need a SAT solver one of these days. 😅 I guess it would probably suffice to write a recursive algorithm that traverses the tree and keeps track of whether every ancestor is an AND or an OR, but I keep getting the "surely this is a solved problem" feeling. :)
Edit: After talking to a couple of friends, I think I may have a sketch of a solution!
Transform the expressions into Conjunctive Normal Form, in which, by definition, every node is in a valid short-circuit position.
Use the Tseitin algorithm to try to avoid exponential blowups in expression size as a result of the CNF transform
For each AND in the tree, sort it in ascending order of cost (i.e. cheapest to the left)
???
Profit!^Weval as usual :)
You should seriously consider compiling the rules (and the predicates). An interpreter is 10-50x slower than machine code for the same thing. This is a good idea if the rule set doesn't change very often. Its even a good idea if the rules can change dynamically because in practice they still don't change very fast, although now your rule compiler has be online. Eh, just makes for a bigger application program and memory isn't much of an issue anymore.
A Boolean expression evaluation using individual machine instructions is even better. Any complex boolean equation can be compiled in branchless sequences of individual machine instructions over the leaf values. No branches, no cache misses; stuff runs pretty damn fast. Now, if you have expensive predicates, you probably want to compile code with branches to skip subtrees that don't affect the result of the expression, if they contain expensive predicates.
Within reason, you can generate any equivalent form (I'd run screaming into the night over the idea of using CNF because it always blows up on you). What you really want is the shortest boolean equation (deepest expression tree) equivalent to what the clients provided because that will take the fewest machine instructions to execute. This may sound crazy, but you might consider exhaustive search code generation, e.g., literally try every combination that has a chance of working, especially if the number of operators in the equation is relatively small. The VLSI world has been working hard on doing various optimizations when synthesizing boolean equations into gates. You should look into the the Espresso hueristic boolean logic optimizer (https://en.wikipedia.org/wiki/Espresso_heuristic_logic_minimizer)
One thing that might drive you expression evaluation is literally the cost of the predicates. if I have formula A and B, and I know that A is expensive to evaluate and usually returns true, then clearly I want to evaluate B and A instead.
You should consider common sub expression evaluation, so that any common subterm is only computed once. This is especially important when one has expensive predicates; you never want to evaluate the same expensive predicate twice.
I implemented these tricks in a PLC emulator (these are basically machines that evaluate buckets [like hundreds of thousands] of boolean equations telling factory actuators when to move) using x86 machine instructions for AND/OR/NOT for Rockwell Automation some 20 years ago. It outran Rockwell's "premier" PLC which had custom hardware but was essentially an interpreter.
You might also consider incremental evaluation of the equations. The basic idea is not to re-evaluate all the equations over and over, but rather to re-evaluate only those equations whose input changed. Details are too long to include here, but a patent I did back then explains how to do it. See https://patents.google.com/patent/US5623401A/en?inventor=Ira+D+Baxter&oq=Ira+D+Baxter
I read Bob Martin's brilliant article on how "Given-When-Then" can actual be compared to an FSM. It got me thinking. Is it OK for a BDD test to have multiple "When"s?
For eg.
GIVEN my system is in a defined state
WHEN an event A occurs
AND an event B occurs
AND an event C occurs
THEN my system should behave in this manner
I personally think these should be 3 different tests for good separation of intent. But other than that, are there any compelling reasons for or against this approach?
When multiple steps (WHEN) are needed before you do your actual assertion (THEN), I prefer to group them in the initial condition part (GIVEN) and keep only one in the WHEN section. This kind of shows that the event that really triggers the "action" of my SUT is this one, and that the previous one are more steps to get there.
Your test would become:
GIVEN my system is in a defined state
AND an event A occurs
AND an event B occurs
WHEN an event C occurs
THEN my system should behave in this manner
but this is more of a personal preference I guess.
If you truly need to test that a system behaves in a particular manner under those specific conditions, it's a perfectly acceptable way to write a test.
I found that the other limiting factor could be in an E2E testing scenario that you would like to reuse a statement multiple times. In my case the BDD framework of my choice(pytest_bdd) is implemented in a way that a given statement can have a singular return value and it maps the then input parameters automagically by the name of the function that was mapped to the given step. Now this design prevents reusability whereas in my case I wanted that. In short I needed to create objects and add them to a sequence object provided by another given statement. The way I worked around this limitation is by using a test fixture(which I named test_context), which was a python dictionary(a hashmap) and used when statements that don't have same singular requirement so the '(when)add object to sequence' step looked up the sequence in the context and appended the object in question to it. So now I could reuse the add object to sequence action multiple times.
This requirement was tricky because BDD aims to be descriptive. So I could have used a single given statement with the pickled memory map of the sequence object that I wanted to perform test action on. BUT would it have been useful? I think not. I needed to get the sequence constructed first and that needed reusable statements. And although this is not in the BDD bible I think in the end it is a practical and pragmatic solution to a very real E2E descriptive testing problem.
I have implemented FIFO semaphores but now I need a way to test/prove that they are working properly. A simple test would be to create some threads that try to wait on a semaphore and then print a message with a number and if the numbers are in order it should be FIFO, but this is not good enough to prove it because that order could have occurred by chance. Thus, I need a better way of testing it.
If necessary locks or condition variables can be used too.
Thanks
What you describe with your sentence "but this is not good enough to prove it because that order could have occurred by chance" is somehow a known dilema.
1) Even if you have a specification, you can not ensure that the specification match your intention. To illustrate this I will take an example from "the limit of correctness". Let's consider a specification for a factorization function that is:
Compute A and B such as A * B = C
But it's not enough as you could have an implementation that returns A=1 and B=C. Adding A,B != 1 can still lead to A=-1 and B=-C, so the only correct specification must state A,B>1. That's just to illustrate how complicated it can be to write a specification that match the real intention.
2) Even having proved an algorithm, still doesn't mean the implementation is correct in practice. This is best illustrated with this quote from Donald Knuth:
Beware of bugs in the above code; I
have only proved it correct, not tried
it.
3) Testing can only reveal the presence of bug, not their absence. This quote goes back to Dijkstra:
Testing can be used to show the
presence of bugs but never to show
their absence.
Conclusion: you are doomed and you will never be 100% sure that your code is correct according to its intent! But stuff aren't that bad. Having a high confidence about the code is usually enough. For instance, if using multiple threads is still not enough for you, you can decide to use fuzzing as well so as to randomize the test execution even more. If your tests always pass, well, you can be pretty confident that your code is good.
because that order could have occurred by chance.
You can run the test a few times, e.g. 10, and test that each time the order was correct. This will ensure that it happened not by chance.
P.S. Multiple threads in a unit test is usually avoided
I need to optimize code to get room for some new code. I do not have the space for all the changes. I can not use code bank switching (80c31 with 64k).
You haven't really given a lot to go on here, but there are two main levels of optimizations you can consider:
Micro-Optimizations:
eg. XOR A instead of MOV A,0
Adam has covered some of these nicely earlier.
Macro-Optimizations:
Look at the structure of your program, the data structures and algorithms used, the tasks performed, and think VERY hard about how these could be rearranged or even removed. Are there whole chunks of code that actually aren't used? Is your code full of debug output statements that the user never sees? Are there functions specific to a single customer that you could leave out of a general release?
To get a good handle on that, you'll need to work out WHERE your memory is being used up. The Linker map is a good place to start with this. Macro-optimizations are where the BIG wins can be made.
As an aside, you could - seriously- try rewriting parts of your code with a good optimizing C compiler. You may be amazed at how tight the code can be. A true assembler hotshot may be able to improve on it, but it can easily be better than most coders. I used the IAR one about 20 years ago, and it blew my socks off.
With assembly language, you'll have to optimize by hand. Here are a few techniques:
Note: IANA8051P (I am not an 8501 programmer but I have done lots of assembly on other 8 bit chips).
Go through the code looking for any duplicated bits, no matter how small and make them functions.
Learn some of the more unusual instructions and see if you can use them to optimize, eg. A nice trick is to use XOR A to clear the accumulator instead of MOV A,0 - it saves a byte.
Another neat trick is if you call a function before returning, just jump to it eg, instead of:
CALL otherfunc
RET
Just do:
JMP otherfunc
Always make sure you are doing relative jumps and branches wherever possible, they use less memory than absolute jumps.
That's all I can think of off the top of my head for the moment.
Sorry I am coming to this late, but I once had exactly the same problem, and it became a repeated problem that kept coming back to me. In my case the project was a telephone, on an 8051 family processor, and I had totally maxed out the ROM (code) memory. It kept coming back to me because management kept requesting new features, so each new feature became a two step process. 1) Optimize old stuff to make room 2) Implement the new feature, using up the room I just made.
There are two approaches to optimization. Tactical and Strategical. Tactical optimizations save a few bytes at a time with a micro optimization idea. I think you need strategic optimizations which involve a more radical rethinking about how you are doing things.
Something I remember worked for me and could work for you;
Look at the essence of what your code has to do and try to distill out some really strong flexible primitive operations. Then rebuild your top level code so that it does nothing low level at all except call on the primitives. Ideally use a table based approach, your table contains stuff like; Input state, event, output state, primitives.... In other words when an event happens, look up a cell in the table for that event in the current state. That cell tells you what new state to change to (optionally) and what primitive(s) (if any) to execute. You might need multiple sets of states/events/tables/primitives for different layers/subsystems.
One of the many benefits of this approach is that you can think of it as building a custom language for your particular problem, in which you can very efficiently (i.e. with minimal extra code) create new functionality simply by modifying the table.
Sorry I am months late and you probably didn't have time to do something this radical anyway. For all I know you were already using a similar approach! But my answer might help someone else someday who knows.
In the whacked-out department, you could also consider compressing part of your code and only keeping some part that is actively used decompressed at any particular point in time. I have a hard time believing that the code required for the compress/decompress system would be small enough a portion of the tiny memory of the 8051 to make this worthwhile, but has worked wonders on slightly larger systems.
Yet another approach is to turn to a byte-code format or the kind of table-driven code that some state machine tools output -- having a machine understand what your app is doing and generating a completely incomprehensible implementation can be a great way to save room :)
Finally, if the code is indeed compiled in C, I would suggest compiling with a range of different options to see what happens. Also, I wrote a piece on compact C coding for the ESC back in 2001 that is still pretty current. See that text for other tricks for small machines.
1) Where possible save your variables in Idata not in xdata
2) Look at your Jmp statements – make use of SJmp and AJmp
I assume you know it won't fit because you wrote/complied and got the "out of memory" error. :) It appears the answers address your question pretty accurately; short of getting code examples.
I would, however, recommend a few additional thoughts;
Make sure all the code is really
being used -- code coverage test? An
unused sub is a big win -- this is a
tough step -- if you're the original
author, it may be easier -- (well, maybe) :)
Ensure the level of "verification"
and initialization -- sometimes we
have a tendency to be over zealous
in insuring we have initialized
variables/memory and sure enough
rightly so, how many times have we
been bitten by it. Not saying don't
initialize (duh), but if we're doing
a memory move, the destination
doesn't need to be zero'd first --
this dovetails with
1 --
Eval the new features -- can an
existing sub be be enhanced to cover
both functions or perhaps an
existing feature replaced?
Break up big code if a piece of the
big code can save creating a new
little code.
or perhaps there's an argument for hardware version 2.0 on the table now ... :)
regards
Besides the already mentioned (more or less) obvious optimizations, here is a really weird (and almost impossible to achieve) one: Code reuse. And with Code reuse I dont mean the normal reuse, but to a) reuse your code as data or b) to reuse your code as other code. Maybe you can create a lut (or whatever static data) that it can represented by the asm hex opcodes (here you have to look harvard vs von neumann architecture).
The other would reuse code by giving code a different meaning when you address it different. Here an example to make clear what I mean. If the bytes for your code look like this: AABCCCDDEEFFGGHH at address X where each letter stands for one opcode, imagine you would now jump to X+1. Maybe you get a complete different functionality where the now by space seperated bytes form the new opcodes: ABC CCD DE EF GH.
But beware: This is not only tricky to achieve (maybe its impossible), but its a horror to maintain. So if you are not a demo code (or something similiar exotic), I would recommend to use the already other mentioned ways to save mem.