Comparing algorithm costs

Usually called "maintenance" of the data structure, the "organization" and "re-organization".

The, "adaptive algorithms" or "adaptive containers", is about data structures reorganizing according to feedback-directed optimization and the like.

It's O[log n] no matter what, so you won't do better, so just use it and be happy.

Ref:

Joe

Post a schematic.

John Larkin Highland Tech Glen Canyon Design Center Lunatic Fringe Electronics

...

Perhaps a book on tree data structures would compare/contrast different types of trees and already have determined which is least compute and/or memory intensive rather than having to experimentally find this out for yourself.

Knuth's TAOCP might be a place to start.

You assume a (potentially) relatively high cost of rebalancing the tree on each insertion or deletion. So, the win for search is offset by that hit -- which only pays off if you then do other searches.

E.g., if you are adding/removing lots of entries in creating a tree, there is a relatively high cost that may never pay off (as you may later only opt to do one "lookup").

And, of course, the order in which you add entries has an optimal case (as well as pathological).

I.e., naive structures can prove to be more efficient (time/space), depending on their contents and manner of construction.

E.g., locating file descriptor 38 is likely more efficient than locating file "38".

That's not the issue. Search/sort/lookup algorithms have all been *generally* characterized as to performance (typ and worst case). But, in practice, how they are actually *used* dramatically alters ACTUAL performance.

Imagine creating a directory (folder) on a storage medium. How should it's "list of contents" be constructed to make for the most efficient use? *You*, the creator, likely know what that usage pattern (and list of potential contents) will likely be. Yet, you (the creator) aren't given any input into the actual implementation chosen for a folder with 4 entries or 400,000. Or, a folder in which some particular entries are "favored" (in use) over others.

You want the structure to be optimized by the *access* order (weighted by the types of accesses), not some arbitrary order that humans conceive of in structuring data (e.g., alphabetic, etc.)

That's exactly right. .

I have a paper copy of Volumes 1-3 bought in the late 1970s. I made much use of it.

Joe

All computer science text books suffer from a lack of practicality. Why isn't Raita used universally -- if it so much more efficient? (esp wrt brute force). Or, Red-Black trees? B trees? H trees? AVL trees? Why so many solutions as their performance characteristics (in the GENERAL sense) can easily be ranked.

As I said, "in practice, how they are actually *used* dramatically alters ACTUAL performance."

Pesky little details like:

- NUMA (let alone NORMA!)

- multiple threads concurrently accessing LARGE data structures

- cost of the "compare" operator relative to the structure itself

- failure resilience etc.

Things that make a "school boy" approach perform terribly in a real-world application.

Knuth documented a huge amount a very long time ago. How much more has really been done since then in terms of algorithm development? I don't have my own copy, but I know several others whose bookshelves are bending under the weight of Knuth.

Back in the real world, I discovered the hard way that unless it is a huge project it is better to keep things simple and not worry to much about efficiency. CPU cycles are cheap compared with the time spent on designing a clever algorithm and making sure that it really works reliably. John

Don Y snipped-for-privacy@foo.invalid wrote: |-------------------------------------------------------------------------| |"Imagine creating a directory (folder)" | |-------------------------------------------------------------------------|

Electronic engineers of course do not need to be told that "directory" is "folder"!

|-------------------------------------------------------------------------| |"How should | |it's "list of contents" be constructed to make for the most efficient | |use? *You*, the creator, likely know what that usage pattern (and | |list of potential contents) will likely be. Yet, you (the creator) | |aren't given any input into the actual implementation chosen for | |a folder with 4 entries or 400,000. [. . .] | |[. . .] | | | |You want the structure to be optimized by the *access* order (weighted by| |the types of accesses)[. ..]" | |-------------------------------------------------------------------------|

I used Wget or whatever in circa 2003 to download the comp.arch.fpga archive that SIGDA used to host. It is not an mbox of many posts - instead each post therein is a single file called like 10001.txt and

They are in many subdirectories, so I decided to burn a version to a CD but with them all in one directory.

This reorganization does not transpire to be an improvement. Contrarily it slows down accesses via e.g. ls * by many MINUTES. So I decided to burn another copy - preserving these subdirectories of SIGDA.

(I actually could not copy them all into the same directory (excluding subdirectories) without renamings, because after I started I detected that a few subdirectories had identically named different posts. SIGDA uniquely named most of them, so I copied most of them into a single directory.) (S.

I take it you had not heard of Knuth. Not a school boy at all. Full professor and known to be the top of that field, and has personally written many very useful applications.

TAOCP covers the foundations at a deep level, so all kinds and classes of solutions are there, compared and contrasted.

Anyway, the various sections are now available online in pdf form, so there is no reason why you cannot read the section about binary trees and the like. This is often good for creating new approaches.

.

As for searching binary trees, here is the Wiki summary: .

Joe

I have first editions (beige linen hardcover) or 1-3. He started on 4 while I was in school but them refocused on TeX (of course, I also have all of the TeX books ("Computers and Typesetting") in hardcover (and those that weren't available as such in paperback)

But disconnected with real-world issues encountered in "appliances". The algorithms are largely described in a sterile, "workstation" (MIX) environment.

Ask yourself how two competing threads would access a Red-Black tree when one is inserting/searching/deleting while the other is performing any of the above operations. Do you put a big lock on the entire structure? And, prevent the task holding the lock from being preempted? What if the tree is stored in FLASH where a write/update is costly (wear) and time consuming?

How does paging affect the performance of various sort algorithms? Caching?

These are real world issues that are completely ignored in the texts.

An algorithm running on an abstract machine is just a teaching tool; it doesn't provide practical information.

There are things that are now commercially viable/practical that weren't really part of the conversation 50 years ago. Public key cryptography post-dated Knuth's work. As did most AI. (My copy of Winston was 8.5x11" photocopied sheets as he was revising the text as he was teaching it -- he welcomed bug reports in class)

E.g., it is now common for cheap processors to have memory onboard, large register files, cache, address spaces with different access characteristics, etc. Memory management units, ECC syndrome handling, floating point and cryptographic accelerators, vector/SIMD processors, etc.

These capabilities alter how you approach problems.

I've scanned all of my textbooks -- bits are much lighter! :>

The bigger risk is the effort subsequent developers will have to invest to understand what you have done and how it can be expected to behave. People "look at" code but seldom with a critical eye (witness the number of latent bugs found in FOSS where dozens/scores/hundreds of eyes have had a chance to discover them -- but failed!)

CPU cycles (and memory, and other resources) should be spent making code more accurate and reliable. Along with adding features and mechanisms that make the *product* more reliable.

A lot depends on scale. Unfortunately, how you start often limits the extent to which you can realistically scale a solution. And, inertia tends to constrain subsequent projects, as well -- as folks try to reuse code and designs. The belief (delusion?) that "it works" is a strong motivator for leaving things as they are.

E.g., this post sought information to help in the design of namespaces -- specifically, (key, capability) tuples. If you expect to have only a few entries in a "Context" (the equivalent of a "folder" within a namespace), then you can use a simple array to store the capabilities with the key acting as an index. And, do a lookup in constant time (so much for the "efficiency" of AVL trees!)

Sharing such an object is considerably easier (less expensive) as you can create a critical region that just includes the actual array access -- any other work can be done before (or after) the critical region. Compare that effort to storing, say, just *10* items in a tree (ANY sort of tree).

I have a "camera" namespace (managed by the camera server and distributed to individual camera clients). it has keys like:

- NW looking East

- NW looking South

- NE looking West

- NE looking South

- SW looking East

- SW looking North ...

- Facing Front Door

- Facing Visitor

- Rooftop PTZ

- Back yard PTZ

- Front yard PTZ

- Garage Door Riser, Left Side

- Garage Door Riser, Right Side

- Garage Door Traveler, Left Side

- Garage Door Traveler, Right Side ...

- Watching Sky

- Watching Mailbox

- Watching Driveway

(I use a lot of cameras as once you can process video imagery, you can do a *lot* with a cheap camera!)

Searching this namespace requires matching strings, even though there are only a few dozen entries. So, operations that access that structure likely take longer than accessing an indexed array.

But, once a particular entry is located, the controller can spawn a "Camera Monitor" with a namespace that consists of just:

- Camera

- PTZ Controller

- IR illuminator binding the corresponding resources derived from the controller's namespace.

And, the "Camera Monitor" *knows* that there will be a successful result when it tries to resolve any of these three names! Not a guarantee that can be made of the controller's namespace. I could just as easily call them 1, 2 and 3 (with a more efficient namespace implementation as an array) with: enum {"Camera"=1, "PTZ Controller", "IR illuminator"} to let the developer dress up his code.

OTOH, imagine having 100,000 tuples in the dictionary. And, keys that want to have special meaning to a developer (beyond simple integers/indices). Now, you need an efficient way to examine those 100,000 keys to locate the tuple of interest (or, determine that it doesn't exist). So, how you share *that* structure becomes a crucial issue (unless you subscribe to the "intermittent, hard to track down, bug" way of coding!)

If the namespace is effectively read-only, then you would approach its design differently than if you expected a large number of insert/delete operations.

If the namespace is stored, *scattered* on rotating rust, there is a cost to accessing it in its entirety and a risk of loss if those operations are interrupted or corrupted.

The developer knows what the range of names/keys will likely be and how they will be accessed. So, *he* should determine how the NameSpace object is instantiated instead of one-size-fits-all. Show me where Knuth addresses any of these real-world issues...

Often, large datasets are broken into a series of smaller subgroups to reduce the size of each directory along the path:

This takes some of the complexity out of the final directory level and handles it in higher level directories in the hierarchy.

The risk with such an approach is the temptation to name the actual entries "1, 2, 3, 4..." in each of the "week" directories. Then, trying to combine them results in a January with five "1" entries (one for the 1st-7th, another for the 8th-14th, etc.)

Many of the BSDs operate repositories of third party applications from which users can fetch "packages" (source or binary). Sadly, these all reside in a ".../distfiles" directory -- thousands of them! So, access to its contents is time consuming (and don't even think of "ls -al"!)

Of course, if you want to mirror said contents, you are stuck with the came constraint!

If the tree is of a bounded size, you could put it into an array instead of off the heap.

A usual idea is figuring out attributes of unbounded size, yet usually _small_. For example, something like map-reduce has expectations that the count of keys is about uniform size and furthermore that it's not a lot, while often there are like "modes: principals, majors, minors, and the long tail".

So, if the attribute has count 1 and sometimes count 2-3, then it might make sense to just make the attribute size

Matters of locality, proximity, and affinity, in many-core architectures which are usual these days since the like of AMD "Bulldozer" after dual-core, makes for that modern architectures like Arm/Thumb, Intel/AMD, or MIPS/RISC-V are much alike in that the neighborhood topology is a thing then that it's always a model of a distributed system.

About "data" and "summary", and, the "tractable", tractable summary, begins usually enough with "counts", and for example about checksums, yet though that "counts" indicate the size, where the size is the usual idea for Big-O, Big-Theta, and Little-Theta notation, of the "asymptotics", since also size is always assumed to grow "unboundedly" in the consideration of the "asymptotics" or the "amortization" over time, which is usually given as the only factor of cost. So, that's not a complete account, and, for example, then whether a linked-list or an array is the better backing for any sort of iterative access and organization and maintenance of a data structure, has that it varies.

Indeed it's so that if the distribution as of what would be a random sample is completely known, then an entirely demonstrably optimum sort of data structure can be made for it. Then, that the _library algorithms_ and the _library data structures_ are instead very common and unspecialized, is about specializations and types in a sense, and probabilistic accounts.

About the massive-wide and the parallel, then how to make it so that the data is stored in summary, then usually for partitions with "uniform random access" by the discrimination of partitions, has that the organization of data, for example in hierarchical naming and directory interfaces or hierarchical time-series data, those being about opposites yet always in terms of each other, is much for summary and statistic.

Then, at least one idea has that any data structure is half-data / half-summary, about usual ideas of state and scope, and the locality of reference, then for example having a system that just constantly updates those and is always re-organizing / re-structuring / re-sorting / re-building, then that reads and writes have the same cost.

A usual account is for making reads free, and consistent, and writes atomic, and independent, vis-a-vis the granular and bulk and items and batches.

So, vis-a-vis the school-boy or class-room accounts, for something like Aho Sethi Ullman and "a parser" vis-a-vis "parsing", modern architecures are themselves models of self-contained distributed systems.

About some ideas from distributed systems in "space and time" of "accesors and mutators" of "vertical and horizontal scaling" of the "inherently serial and embarrassingly parallel" are the "eventually consistent" vis-a-vis the "optimistic locking" about "vending universally unique ID's" and "retry-logic", issues that arise in any model of coherency and consistency about "sources of truth and a source of truth" and "points of change and propagation of change".

Don Y snipped-for-privacy@foo.invalid wrote: |----------------------------------------------------------------| |"The bigger risk is the effort subsequent developers will have | |to invest to understand what you have done and how it can | |be expected to behave. People "look at" code but seldom | |with a critical eye (witness the number of latent bugs found | |in FOSS where dozens/scores/hundreds of eyes have had a chance | |to discover them -- but failed!)" | |----------------------------------------------------------------|

True. Cf. Knuth said to write a paper assuming that a reader does not bother to read its equations before the 2nd time thru.

|----------------------------------------------------------------| |"CPU cycles (and memory, and other resources) should be spent | |making code more accurate and reliable. Along with adding | |features and mechanisms that make the *product* more reliable. | | | |[E.g., I treat memory much like a disk drive. If a page | |starts throwing too many ECC errors, then I mark it as | |defective and map a "working" page in its place. If | |too many pages are retired in this way, I alert the user. | |Much like SMART does.]" | |----------------------------------------------------------------|

Good.

|-----------------------------------------------------------------| |"Show me where Knuth addresses any of these real-world issues..."| |-----------------------------------------------------------------|

These very big very late books would be even bigger later if Knuth would add more considerations to them. (S.

There's nothing that says how large a book can be. Or, how many "spines" it can have.

Knuth published collections of lecture notes, much later in his career. But, they touch on issues that aren't really related to real implementations. I.e., more "school boy" approach where your hands never really get dirty.

It is not clear why you ask and what you really hipe/expect from answers. You clearly know a lot, but you frequently ask questions as if you know nothing. Knuth was/is real programmer, but he also is a scientist. In science we abstract, throw out irrelevant or problemantic details to build a simplified model. Such models frequently are useful, but do not give you full answer. In your problem, it is you who (maybe) has relevant information, it is your system and we know nothing about it beyond few pieces of information that you gave. Distribution of keys and lookup is closely coupled to design and tasks assigned to your system. You choose storage medium (RAM, spinning disc, flash, whatever).

Classic texts like Knuth give you general properties. Those allow you to make useful inferences once you add specific information. So, it is practical information. But not a complete answer, you still need to do your job.

Concerning concurrent access, there is a business of lockless data structures, if you need them classic works will not help you. OTOH, you may decide to use locks. Specifics of locks are almost orthogonal to search algorithms, so it does not matter that classic texts do not consider interaction of locks and search. More precisely, knowing algorithm and expected usage you should be able to decide if locking is needed and minimal scope of locks. Similarly given specific hardware you should be able to estimate how long you need to keep the locks. The rest is general system design.

Concerning structures in flash, that are academic works about filesystems for CD-RW and flash. For AVL trees the answer looks obvious: you do not put frequently modified AVL tree in flash.

If you ask about caching and paging, the core info is implicit in classic texts: properly sized hash table on average needs small constant number of storage accesses and it is reasonable to expect that most of accesses are misses. In case of trees you can hope that top part of the tree will be in the cache. I am not sure if Knuth discusses search on disc, but if not other classic texts do. Think page = unit of information transferred to/from disc and you should see relevance of this to paging. OTOH there is tendency to put plenty of RAM in modern system, so that there is no need for paging. It is for you to decide if you need/want paging.