India has a Three-Body Problem

When I moved to Bangalore six years ago, I had no interest in India’s garbage management problem. I moved here to build a software company, not to muck about with garbage bins. But every time I would fly between the subcontinent and the Americas, I would find relief on my home continent and exhaustion on my continent of immigration. Living in a giant garbage heap is mentally taxing. Every moment spent outdoors is either an angsty mental rundown of how I might help, complete with acute feelings of powerlessness, or searching for someone to blame (my brain’s lazy personal favourite is a generic and completely unhelpful “the middle class”).

Reprieve comes in the form of resignation as the mind finally comes to accept a dangerous and unsustainable situation as inevitable. Telling yourself “this is just the way it is” at least allows you to enjoy a cup of tea or a single workday without obsessing about the garbage fire you will inevitably inhale on the bicycle ride home from the office.

But that doesn’t work for very long. The workday eventually ends and that bicycle ride inevitably happens. As you hold your breath and ride through the plume of smoke from a smouldering pile of trash on the side of the road, you ask yourself why in the sweet fuck do I even live here? And the answers instantly follow. Your friends are here, your home is here, your job is here. On the smallest scales some part of you belongs in India and on the largest possible scale you recognize that India’s problems of today are Earth’s problems of tomorrow. You can’t ignore them forever.

So you sit down and think about those problems. A lot.

What is garbage?

It may seem like a silly question but all sorts of variations have been asked of me over the years. “Why does garbage stink?” “Why can’t we just focus more attention on reuse?” and “Where do the costs of sanitation come from?” come to mind. If you’ve ever thrown something in the trash bin without asking yourself just where is this going to end up? you don’t really know what garbage is. The answer to this question changes with almost every piece of trash, with almost every bin, and definitely with every city.

Garbage is anything society’s majority decides it doesn’t want anymore. This extremely poisonous polystyrene Starbucks lid has served its purpose! I have a litre of burnt coffee in my stomach which means it’s definitely time to poop. Thanks for the assistance and off you go, immortal little lid, to know every worm that crawls by you until the sun of the milky way engulfs the Earth in five billion years and finally incinerates you. While this may be the future the majority plans, our little lid might never make it there. The lowest classes of human society repurpose garbage, sell garbage, and burn garbage to keep warm. The convenience of the healthy, educated elite are a disgrace to our species but, unexpectedly, the response of those at the bottom of the pyramid actually informs the way we should behave. This is because they know what garbage is.

Back in Canada, we don’t know what garbage is

One difficulty with living in a rich country backed by a huge resource economy is that it’s pretty easy to get self-righteous about how people in that country live, even if that way of life is far from perfect. It wasn’t that long ago that the garbage dump outside my hometown still burnt most of its garbage in open fires. (No one in southwest Saskatchewan bothers with that “landfill” euphemism.) It was on one of my many trips back and forth between India and Canada that it struck me how poorly we deal with waste in Canada.

Setting an example matters. If a country with the resources, time, money, and literacy of Canada can’t figure out what to do with our plastic bags or Styrofoam (Thermocol) it’s hard to imagine how any other country could do it. Canada leans on its unbelievable land mass and access to cheap fuel when it chooses to dump garbage in holes. As the world’s population distributes itself more evenly over the coming generations the sweep-it-under-the-rug approach will quickly bring the problems which plague India today to every other country on the planet. The problems of India today are the problems of Earth tomorrow.

Because Level 4 countries like Canada generally don’t appreciate garbage, Canada needs countries like India and China to show it the way. Canada has 3.7 crore (37 million) citizens; India has 134 crore (1.3 billion). Canada’s country-wide garbage solutions need to scale thirty six times over or they are a failure on a global scale.

Thankfully, India’s garbage crisis is a forcing function. Every day the garbage piles up inside and around India’s cities, the weight of it poisons us. Garbage on the street is an opportunity for the average citizen to stop and think about where her plastic milk packets go after they leave her house. Paradoxically, this is India’s opportunity to lead the rest of the globe.

Garbage is a small riddle

As an individual human being, there are only two steps to understanding our garbage. First, we need to deconstruct it. Second, we need to identify each component.

My friend’s question why does garbage stink? has an answer he could have puzzled out himself and it is arguably the first component of our deconstruction: some garbage is organic. Things we consume which were once alive are still alive on a microscopic level when they come to us. Living things stink. Or they will, eventually. The nice thing about this entire category is that every single item in it falls into the broad category of Organic. Biodegradable. Compost.

In India, biodegradable is often dubbed wet waste but I have issues with that nomenclature. It confuses people. A plastic tray with panipuri juice in it is certainly wet but it is in no way wet waste. If people understand that the green bin only means biodegradable, they’re less likely to throw plastic in there. Prior to our most recent train journey we took note of the waste signage in the Chennai Central railway station. The signage had other issues (which we’ll come to later) but the translations were on-point: biodegradable waste is सड़नशील कचरा in Hindi and… well, Preethi tells me those other words are the right ones in Tamil, whatever they say. The value of the word biodegradable is that it elevates the conversation. Are supposedly biodegradable plastics okay? Are coconut shells biodegradable enough, since they’re more like wood than food? The Wet Waste terminology doesn’t beg these questions.

Our second component comes from the other end of the biodegradability spectrum: plastics. The ubiquitous blue bin has come to mean recyclable material in general but it is plastic that stands apart from all other materials. No matter how many times you reuse that plastic bag, plastic chopstick, or plastic toy it will eventually have an end and that end should neither be the garbage dump nor a trash fire. Plastic needs to be recycled or broken down or we live with it forever.

The Three-Body Problem

This is where India gets stuck. There are many different methods of deconstruction and most of them don’t work. Any system which acknowledges the importance of our first two categories (organic and plastic) inherently creates a third: Everything Else.

This often stems from an enthusiasm to solve the garbage crisis. A city like Jaipur sees the problem of unsegregated waste and attacks it head-on… with the wrong weapon.

A garbage lorry in Jaipur

We recently visited the Jaipur Literature Festival and found ourselves pleasantly surprised by the city’s ubiquitous, segregated public waste system. Jaipur has segregated bins everywhere… but only blue and green bins and only blue and green garbage trucks. Blue/Green, Two-Body segregated bins are a step in the right direction. The citizens of Jaipur see these green and blue bins every day. Whether they respect the bins’ colours or not, a part of their subconscious starts to acknowledge that not all garbage is created equal.

A single look in these bins, however, will show you just how horribly ineffective a Two-Body system is. The green bin is full of dirty styrofoam plates. The blue bin is full of dirty, mixed waste. These broad, mutually exclusive strokes of Green = Wet Waste and Blue = Dry Waste mean that we must choose one of these two categories for Everything Else. A wet plastic dish full of panipuri juice is the least of our troubles. Where should I put a snot-filled tissue? A dirty diaper? Roadkill? Paint chips? Construction rubble? A used sanitary napkin?

Yeah. I don’t know, either. You need three bins.

This question of “other garbage” helped us persuade the building management of our Bangalore apartment complex to let us install a three-bin system: green for organic, blue for recyclables, red for everything else. “Everything else” is labelled reject on our bin but that’s just a fancy way of saying old-fashioned garbage. We actually encouraged our building-mates to put everything in the red bin until they were absolutely certain it belonged in the green or blue bin. A whole bin of clean compost is worthless if a single person poisons it. A whole bin of clean recycling is worthless if a single person dumps chutney all over it.

For months, before we were allowed to install the bins, the building manager repeatedly insisted that “a three-bin system will be too complicated” and “the municipal corporation waste-pickers don’t know what to do with three different bins.”

Over the years before Preethi and I installed these bins, we had heard every brand of argument against segregating garbage: it’s too complicated, the waste-pickers just throw it into one pile anyway, the system is corrupt, the residents / staff / citizens won’t follow the rules, and on and on and on. I’ve fought this battle so many times I’ve even codified my arguments for a Waste Maturity Model into a presentation. Thanks to battling these talking points with our families, friends, and coworkers, we were well-equipped to fend off the argument that the combination of a green and blue bin would be sufficient.

In the end, where does a diaper go? You need three bins.

Confusing your citizens in two easy steps!

On our way back from Jaipur to Chennai, we flew through Bangalore and it gave us a chance to see three different cities’ broken waste segregation messages.

Bangalore’s useless airport segregation completely flies in the face of the actual state guidelines

Bangalore actually has brilliant state-wide waste segregation guidelines, published in a dozen languages. They answer questions I wouldn’t think to ask (where do my coconut shells go?) and they are actually written into law.

Despite the law, the Bangalore International Airport thinks a 4-bin system is better than a 3-bin system. And, much to our chagrin, none of those bins is for reject garbage. Where do I put my snotty tissue in the Bangalore airport? Is a snotty tissue food, plastic, paper, or cans? None of the above. Hold onto it until you get to Chennai, I guess.

Chennai, on the other hand, has functional segregation at the airport. Hooray! For some unknown reason, they eschewed the standard international green/blue/black colours (or the slightly modified green/blue/red used in Karnataka). But that’s okay. At least I know where to dump my half-eaten dosa, the plastic spoon the chutney came with, and the wrapper with chocolate all over it that melted in my pocket during the flight.

Chennai airport has real waste segregation but the city has zero

Unfortunately, as soon as you get into the city the only bins you will find are for “non-recyclable waste” — giant green dumpsters akin to those of mid-twentieth-century American cities. If you want to figure out compost or recycling as a citizen of Chennai, you’re on your own.

Moving from one city to another can often be as drastic as moving from one country to another but moving from the airport to the office shouldn’t be. It’s little wonder that citizens are confused, waste-pickers are frustrated, and government officials feel overwhelmed.

India can lead. India must lead.

It is the perfect time for Indian states to unify on their understanding of the top three categories of waste. Whether we choose to label them wet, dry, and reject or organic, recyclable, and mixed or green, blue, and red or any other three titles, unity from the top will help us pick away at the many difficulties plaguing India’s streets and water bodies.

Over time, these three categories will spawn other categories. Separating plastics from paper from metal from glass from e-waste is a good way to split up the blue bin full of generic recyclables. If you want to split up the city’s plastic, there are at least seven plastic recycling symbols to go by. There are over forty international recycling symbols.

Baby steps, though. Keep the plastic out of the green bin and the chicken grease out of the blue bin. Then maybe we can discuss the road to Zero Waste.

Waste management is a form of project management. The project is massive, distributed, and constantly moving. It is global in scale and influenced by every single human being on Earth. Once it happens, successful waste management in India and China will be the gold standard for the rest of the globe.

These are the biggest projects. If we can do it here, we can do it anywhere.

India has a Three-Body Problem was originally published in Siggu on Medium, where people are continuing the conversation by highlighting and responding to this story.

Fast Sudoku Solver in Haskell #3: Picking the Right Data Structures

In the previous part in this series of posts, we optimized the simple Sudoku solver by implementing a new strategy to prune cells, and were able to achieve a speedup of almost 200x. Afterwards, we profiled the solution and found that there were bottlenecks in the program, leading to a slowdown. In this post, we are going to follow the profiler and use the right Data Structures to improve the solution further and make it faster.

This is the third post in a series of posts:

  1. Fast Sudoku Solver in Haskell #1: A Simple Solution
  2. Fast Sudoku Solver in Haskell #2: A 200x Faster Solution
  3. Fast Sudoku Solver in Haskell #3: Picking the Right Data Structures

Discuss this post on r/haskell.

Quick Recap

Sudoku is a number placement puzzle. It consists of a 9x9 grid which is to be filled with digits from 1 to 9 such that each row, each column and each of the nine 3x3 sub-grids contain all the digits. Some of the cells of the grid come pre-filled and the player has to fill the rest.

In the previous post, we improved the performance of the simple Sudoku solver by implementing a new strategy to prune cells. This new strategy found the digits which occurred uniquely, in pairs, or in triplets and fixed the cells to those digits. It led to a speedup of about 200x over our original naive solution. This is our current run1 time for solving all the 49151 17-clue puzzles:

$ cat sudoku17.txt | time stack exec sudoku > /dev/null
      258.97 real       257.34 user         1.52 sys

Let’s try to improve this time.2

Profile Twice, Code Once

Instead of trying to guess how to improve the performance of our solution, let’s be methodical about it. We start with profiling the code to find the bottlenecks. Let’s compile and run the code with profiling flags:

$ stack build --profile
$ head -1000 sudoku17.txt | stack exec -- sudoku +RTS -p > /dev/null

This generates a file with the profiling output. Here are the top seven Cost Centres3 from the file (cleaned for brevity):

Cost Centre Src %time %alloc
exclusivePossibilities Sudoku.hs:(49,1)-(62,26) 18.9 11.4
pruneCellsByFixed.pruneCell Sudoku.hs:(75,5)-(76,36) 17.7 30.8
exclusivePossibilities.\.\ Sudoku.hs:55:38-70 11.7 20.3
fixM.\ Sudoku.hs:13:27-65 10.7 0.0
== Sudoku.hs:15:56-57 5.6 0.0
pruneGrid' Sudoku.hs:(103,1)-(106,64) 5.0 6.7
pruneCellsByFixed Sudoku.hs:(71,1)-(76,36) 4.5 5.0
exclusivePossibilities.\ Sudoku.hs:58:36-68 3.4 2.5

Cost Centre points to a function, either named or anonymous. Src gives the line and column numbers of the source code of the function. %time and %alloc are the percentages of time spent and memory allocated in the function, respectively.

We see that exclusivePossibilities and the nested functions inside it take up almost 34% time of the entire run time. Second biggest bottleneck is the pruneCell function inside the pruneCellsByFixed function.

We are going to look at exclusivePossibilities later. For now, it is easy to guess the possible reason for pruneCell taking so much time. Here’s the code for reference:

pruneCell uses Data.List.\\ to find the difference of the cell’s possible digits and the fixed digits in the cell’s block. In Haskell, lists are implemented as singly linked lists. So, finding the difference or intersection of two lists is O(n2), that is, quadratic asymptotic complexity. Let’s tackle this bottleneck first.

A Set for All Occasions

What is a efficient data structure for finding differences and intersections? Why, a Set of course! A Set stores unique values and provides fast operations for testing membership of its elements. If we use a Set to represent the possible values of cells instead of a List, the program should run faster. Since the possible values are already unique (19), it should not break anything.

Haskell comes with a bunch of Set implementations:

However, a much faster implementation is possible for our particular use-case. We can use a BitSet.

A BitSet uses bits to represent unique members of a Set. We map values to particular bits using some function. If the bit corresponding to a particular value is set to 1 then the value is present in the Set, else it is not. So, we need as many bits in a BitSet as the number of values in our domain, which makes is difficult to use for generic problems. But, for our Sudoku solver, we need to store only the digits 19 in the Set, which make BitSet very suitable for us. Also, the Set operations on BitSet are implemented using bit-level instructions in hardware, making them much faster than those on the other data structure listed above.

In Haskell, we can use the Data.Word module to represent a BitSet. Specifically, we can use the Data.Word.Word16 type which has sixteen bits because we need only nine bits to represent the nine digits. The bit-level operations on Word16 are provided by the Data.Bits module.

Bit by Bit, We Get Faster

First, we replace List with Word16 in the Cell type and add a helper function:

Then we replace Int related operations with bit related ones in the read and show functions:

We set the same bits as the digits to indicate the presence of the digits in the possibilities. For example, for digit 1, we set the bit 1 so that the resulting Word16 is 0000 0000 0000 0010 or 2. This also means, for fixed cells, the value is count of the zeros from right.

The change in the exclusivePossibilities function is pretty minimal:

In the nested folding step, instead of folding over the possible values of the cells, now we fold over the digits from 1 to 9 and insert the entry in the map if the bit corresponding to the digit is set in the possibilities. And as the last step, we convert the exclusive possibilities to Word16 by folding them, starting with zero. As example in the REPL should be instructive:

This is the same example row as the last time. And it returns same results, excepts as a list of Word16 now.

Now, we change makeCell to use bit operations instead of list ones:

And we change cell pruning functions too:

Notice how the list difference and intersection functions are replaced by Data.Bits functions. Specifically, list difference is replace by bitwise-and of the bitwise-complement, and list intersection is replaced by bitwise-and.

We make a one-line change in the isGridInvalid function to find empty possible cells using bit ops:

And finally, we change the nextGrids functions to use bit operations:

possibilityCount now uses Data.Bits.popCount to count the number of bits set to 1. fixCell now chooses the first set bit from right as the digit to fix. Rest of the code stays the same. Let’s build and run it:

$ stack build
$ cat sudoku17.txt | time stack exec sudoku > /dev/null
       69.44 real        69.12 user         0.37 sys

Wow! That is almost 3.7x faster than the previous solution. It’s a massive win! But let’s not be content yet. To the profiler again!4

Back to the Profiler

Running the profiler again gives us these top six culprits:

Cost Centre Src %time %alloc
exclusivePossibilities Sudoku.hs:(57,1)-(74,26) 25.2 16.6
exclusivePossibilities.\.\ Sudoku.hs:64:23-96 19.0 32.8
fixM.\ Sudoku.hs:15:27-65 12.5 0.1
pruneCellsByFixed Sudoku.hs:(83,1)-(88,36) 5.9 7.1
pruneGrid' Sudoku.hs:(115,1)-(118,64) 5.0 8.6

Hurray! pruneCellsByFixed.pruneCell has disappeared from the list of top bottlenecks. Though exclusivePossibilities still remains here as expected.

exclusivePossibilities is a big function. The profiler does not really tell us which parts of it are the slow ones. That’s because by default, the profiler only considers functions as Cost Centres. We need to give it hints for it to be able to find bottlenecks inside functions. For that, we need to insert Cost Centre annotations in the code:

Here, {-# SCC "" #-} is a Cost Centre annotation. "" is the name we choose to give to this Cost Centre.

After profiling the code again, we get a different list of bottlenecks:

Cost Centre Src %time %alloc
exclusivePossibilities.\.\ Sudoku.hs:(64,23)-(66,31) 19.5 31.4
fixM.\ Sudoku.hs:15:27-65 13.1 0.1
pruneCellsByFixed Sudoku.hs:(85,1)-(90,36) 5.4 6.8
pruneGrid' Sudoku.hs:(117,1)-(120,64) 4.8 8.3 Sudoku.hs:59:27-36 4.3 10.7
EP.Map.filter1 Sudoku.hs:70:35-61 4.2 0.5
chunksOf Data/List/Split/Internals.hs:(514,1)-(517,49) 4.1 7.4
exclusivePossibilities.\ Sudoku.hs:71:64-96 4.0 3.4
EP.filter Sudoku.hs:60:30-54 2.9 3.4
EP.foldl Sudoku.hs:(61,29)-(69,15) 2.8 1.8
exclusivePossibilities Sudoku.hs:(57,1)-(76,26) 2.7 1.9
chunksOf.splitter Data/List/Split/Internals.hs:(516,3)-(517,49) 2.5 2.7

So almost one-fifth of the time is actually going in this nested one-line anonymous function inside exclusivePossibilities:

But we are going to ignore it for now.

If we look closely, we also find that around 17% of the run time now goes into list traversal and manipulation. This is in the functions pruneCellsByFixed, pruneGrid', chunksOf and chunksOf.splitter, where the first two are majorly list traversal and transposition, and the last two are list splitting. Maybe it is time to get rid of lists altogether?

Vectors of Speed

Vector is a Haskell library for working with arrays. It implements very performant operations for integer-indexed array data. Unlike the lists in Haskell which are implemented as singly linked lists, vectors are stored in a contiguous set of memory locations. This makes random access to the elements a constant time operation. The memory overhead per additional item in vectors is also much smaller. Lists allocate memory for each item in the heap and have pointers to the memory locations in nodes, leading to a lot of wasted memory in holding pointers. On the other hand, operations on lists are lazy, whereas, operations on vectors are strict, and this may need to useless computation depending on the use-case5.

In our current code, we represent the grid as a list of lists of cells. All the pruning operations require us to traverse the grid list or the row lists. We also need to transform the grid back-and-forth for being able to use the same pruning operations for rows, columns and sub-grids. The pruning of cells and the choosing of pivot cells also requires us to replace cells in the grid with new ones, leading to a lot of list traversals.

To prevent all this linear-time list traversals, we can replace the nested list of lists with a single vector. Then all we need to do it to go over the right parts of this vector, looking up and replacing cells as needed. Since both lookups and updates on vectors are constant time, this should lead to a speedup.

Let’s start by changing the grid to a vector of cells.:

Since we plan to traverse different parts of the same vector, let’s define these different parts first:

We define a type for cell indices as a list of integers. Then we create three lists of cell indices: all row indices, all column indices, and all sub-grid indices. Let’s check these out in the REPL:

We can verify manually that these indices are correct.

Read and show functions are easy to change for vector:

readGrid simply changes to work on a single vector of cells instead of a list of lists. Show functions have a pretty minor change to do lookups from a vector using the row indices and the (!) function. The (!) function is the vector indexing function which is similar to the (!!) function, except it executes in constant time.

The pruning related functions are rewritten for working with vectors:

All the three functions now take the grid and the cell indices instead of a list of cells, and use the cell indices to lookup the cells from the grid. Also, instead of using the traverse function as earlier, now we use the Control.Monad.foldM function to fold over the cell-index-and-cell tuples in the context of the Maybe monad, making changes to the grid directly.

We use the replaceCell function to replace cells at an index in the grid. It is a simple wrapper over the vector update function Data.Vector.//. Rest of the code is same in essence, except a few changes to accommodate the changed function parameters.

pruneGrid' function does not need to do transpositions and back-transpositions anymore as now we use the cell indices to go over the right parts of the grid vector directly:

Notice that the traverse function here is also replaced by the Control.Monad.foldM function.

Similarly, the grid predicate functions change a little to go over a vector instead of a list of lists:

And finally, we change the nextGrids function to replace the list related operations with the vector related ones:

We also switch the replace2D function which went over the entire list of lists of cells to replace a cell, with the vector-based replaceCell function.

All the required changes are done. Let’s do a run:

$ stack build
$ cat sudoku17.txt | time stack exec sudoku > /dev/null
       88.53 real        88.16 user         0.41 sys

Oops! Instead of getting a speedup, our vector-based code is actually 1.3x slower than the list-based code. How did this happen? Time to bust out the profiler again!

Revenge of the (==)

Profiling the current code gives us the following hotspots:

Cost Centre Src %time %alloc
>>= Data/Vector/Fusion/Util.hs:36:3-18 52.2 51.0
basicUnsafeIndexM Data/Vector.hs:278:3-62 22.2 20.4
exclusivePossibilities Sudoku.hs:(75,1)-(93,26) 6.8 8.3
exclusivePossibilities.\.\ Sudoku.hs:83:23-96 3.8 8.8
pruneCellsByFixed.fixeds Sudoku.hs:105:5-77 2.0 1.7

We see a sudden appearance of (>>=) from the Data.Vector.Fusion.Util module at the top of the list, taking more than half of the run time. For more clues, we dive into the detailed profiler report and find this bit:

Cost Centre Src %time %alloc
pruneGrid Sudoku.hs:143:1-27 0.0 0.0
  fixM Sudoku.hs:16:1-65 0.1 0.0
    fixM.\ Sudoku.hs:16:27-65 0.2 0.1
      == Data/Vector.hs:287:3-50 1.0 1.4
        >>= Data/Vector/Fusion/Util.hs:36:3-18 51.9 50.7
          basicUnsafeIndexM Data/Vector.hs:278:3-62 19.3 20.3

Here, the indentation indicated nesting of operations. We see that both the (>>=) and basicUnsafeIndexM functions — which together take around three-quarter of the run time — are being called from the (==) function in the fixM function6. It seems like we are checking for equality too many times. Here’s the usage of the fixM for reference:

In pruneGrid, we run pruneGrid' till the resultant grid settles, that is, the grid computed in a particular iteration is equal to the grid in the previous iteration. Interestingly, we do the same thing in pruneCells too. We equate the whole grid to check for settling of each block of cells. This is the reason of the slowdown.

One Function to Prune Them All

Why did we add fixM in the pruneCells function at all? Quoting from the previous post,

We need to run pruneCellsByFixed and pruneCellsByExclusives repeatedly using fixM because an unsettled row can lead to wrong solutions.

Imagine a row which just got a 9 fixed because of pruneCellsByFixed. If we don’t run the function again, the row may be left with one non-fixed cell with a 9. When we run this row through pruneCellsByExclusives, it’ll consider the 9 in the non-fixed cell as a Single and fix it. This will lead to two 9s in the same row, causing the solution to fail.

So the reason we added fixM is that, we run the two pruning strategies one-after-another. That way, they see the cells in the same block in different states. If we were to merge the two pruning functions into a single one such that they work in lockstep, we would not need to run fixM at all!

With this idea, we rewrite pruneCells as a single function:

We have merged the two pruning functions almost blindly. The important part here is the nested pruneCell function which uses monadic bind (>>=) to ensure that cells fixed in the first step are seen by the next step. Merging the two functions ensures that both strategies will see same Exclusives and Fixeds, thereby running in lockstep.

Let’s try it out:

$ stack build
$ cat sudoku17.txt | time stack exec sudoku > /dev/null
      57.67 real        57.12 user         0.46 sys

Ah, now it’s faster than the list-based implementation by 1.2x7. Let’s see what the profiler says:

Cost Centre Src %time %alloc
exclusivePossibilities.\.\ Sudoku.hs:82:23-96 15.7 33.3
pruneCells Sudoku.hs:(101,1)-(126,53) 9.6 6.8
pruneCells.pruneCell Sudoku.hs:(108,5)-(109,83) 9.5 2.1
basicUnsafeIndexM Data/Vector.hs:278:3-62 9.4 0.5
pruneCells.pruneCell.\ Sudoku.hs:109:48-83 7.6 2.1
pruneCells.cells Sudoku.hs:103:5-40 7.1 10.9
exclusivePossibilities.\ Sudoku.hs:87:64-96 3.5 3.8
EP.Map.filter1 Sudoku.hs:86:35-61 3.0 0.6
>>= Data/Vector/Fusion/Util.hs:36:3-18 2.8 2.0
replaceCell Sudoku.hs:59:1-45 2.5 1.1
EP.filter Sudoku.hs:78:30-54 2.4 3.3
primitive Control/Monad/Primitive.hs:195:3-16 2.3 6.5

The double nested anonymous function mentioned before is still the biggest culprit but fixM has disappeared from the list. Let’s tackle exclusivePossibilities now.

Rise of the Mutables

Here’s exclusivePossibilities again for reference:

Let’s zoom into lines 6–14. Here, we do a fold with a nested fold over the non-fixed cells of the given block to accumulate the mapping from the digits to the indices of the cells they occur in. We use a Data.Map.Strict map as the accumulator. If a digit is not present in the map as a key then we add a singleton list containing the corresponding cell index as the value. If the digit is already present in the map then we prepend the cell index to the list of indices for the digit. So we end up “mutating” the map repeatedly.

Of course, it’s not actual mutation because the map data structure we are using is immutable. Each change to the map instance creates a new copy with the addition, which we thread through the fold operation, and we get the final copy at the end. This may be the reason of the slowness in this section of the code.

What if, instead of using an immutable data structure for this, we used a mutable one? But how can we do that when we know that Haskell is a pure language? Purity means that all code must be referentially transparent, and mutability certainly isn’t. It turns out, there is an escape hatch to mutability in Haskell. Quoting the relevant section from the book Real World Haskell:

Haskell provides a special monad, named ST, which lets us work safely with mutable state. Compared to the State monad, it has some powerful added capabilities.

  • We can thaw an immutable array to give a mutable array; modify the mutable array in place; and freeze a new immutable array when we are done.
  • We have the ability to use mutable references. This lets us implement data structures that we can modify after construction, as in an imperative language. This ability is vital for some imperative data structures and algorithms, for which similarly efficient purely functional alternatives have not yet been discovered.

So if we use a mutable map in the ST monad, we may be able to get rid of this bottleneck. But, we can actually do better! Since the keys of our map are digits 19, we can use a mutable vector to store the indices. In fact, we can go one step even further and store the indices as a BitSet as Word16 because they also range from 1 to 9, and are unique for a block. This lets us use an unboxed mutable vector. What is unboxing you ask? Quoting from the GHC docs:

Most types in GHC are boxed, which means that values of that type are represented by a pointer to a heap object. The representation of a Haskell Int, for example, is a two-word heap object. An unboxed type, however, is represented by the value itself, no pointers or heap allocation are involved.

When combined with vector, unboxing of values means the whole vector is stored as single byte array, avoiding pointer redirections completely. This is more memory efficient and allows better usage of caches8. Let’s rewrite exclusivePossibilities using ST and unboxed mutable vectors.

First we write the core of this operation, the function cellIndicesList which take a list of cells and returns the digit to cell indices mapping. The mapping is returned as a list. The zeroth value in this list is the indices of the cells which have 1 as a possible digit, and so on. The indices themselves are packed as BitSets. If the bit 1 is set then the first cell has a particular digit. Let’s say it returns [0,688,54,134,0,654,652,526,670]. In 10-bit binary it is:

[0000000000, 1010110000, 0000110110, 0010000110, 0000000000, 1010001110, 1010001100, 1000001110, 1010011110]

We can arrange it in a table for further clarity:

Digits Cell 9 Cell 8 Cell 7 Cell 6 Cell 5 Cell 4 Cell 3 Cell 2 Cell 1
1 0 0 0 0 0 0 0 0 0
2 1 0 1 0 1 1 0 0 0
3 0 0 0 0 1 1 0 1 1
4 0 0 1 0 0 0 0 1 1
5 0 0 0 0 0 0 0 0 0
6 1 0 1 0 0 0 1 1 1
7 1 0 1 0 0 0 1 1 0
8 1 0 0 0 0 0 1 1 1
9 1 0 1 0 0 1 1 1 1

If the value of the intersection of a particular digit and a particular cell index in the table is set to 1, then the digit is a possibility in the cell, else it is not. Here’s the code:

The whole mutable code runs inside the runST function. runST take an operation in ST monad and executes it, making sure that the mutable references created inside it cannot escape the scope of runST. This is done using a type-system trickery called Rank-2 types.

Inside the ST operation, we start with creating a mutable vector of Word16s of size 9 with all its values initially set to zero. We also initialize a mutable reference to keep track of the cell index we are on. Then we run two nested for loops, going over each cell and each digit 19, setting the right bit of the right index of the mutable vector. During this, we mutate the vector directly using the Data.Vector.Unboxed.Mutable.unsafeModify function. At the end of the ST operation, we freeze the mutable vector to return an immutable version of it. Outside runST, we convert the immutable vector to a list. Notice how this code is quite similar to how we’d write it in imperative programming languages like C or Java9.

It is easy to use this function now to rewrite exclusivePossibilities:

We replace the nested two-fold operation with cellIndicesList. Then we replace some map related function with the corresponding list ones because cellIndicesList returns a list. We also replace the length function call on cell indices with Data.Bits.popCount function call as the indices are represented as Word16 now.

That is it. Let’s build and run it now:

$ stack build
$ cat sudoku17.txt | time stack exec sudoku > /dev/null
      35.04 real        34.84 user         0.24 sys

That’s a 1.6x speedup over the map-and-fold based version. Let’s check what the profiler has to say:

Cost Centre Src %time %alloc
cellIndicesList.\.\ Sudoku.hs:(88,11)-(89,81) 10.7 6.0
primitive Control/Monad/Primitive.hs:195:3-16 7.9 6.9
pruneCells Sudoku.hs:(113,1)-(138,53) 7.5 6.4
cellIndicesList Sudoku.hs:(79,1)-(91,40) 7.4 10.1
basicUnsafeIndexM Data/Vector.hs:278:3-62 7.3 0.5
pruneCells.pruneCell Sudoku.hs:(120,5)-(121,83) 6.8 2.0
exclusivePossibilities Sudoku.hs:(94,1)-(104,26) 6.5 9.7
pruneCells.pruneCell.\ Sudoku.hs:121:48-83 6.1 2.0
cellIndicesList.\ Sudoku.hs:(83,42)-(90,37) 5.5 3.5
pruneCells.cells Sudoku.hs:115:5-40 5.0 10.4

The run time is spread quite evenly over all the functions now and there are no hotspots anymore. We stop optimizating at this point10. Let’s see how far we have come up.

Comparison of Implementations

Below is a table showing the speedups we got with each new implementation:

Implementation Run Time (s) Incremental Speedup Cumulative Speedup
Simple 47450 1x 1x
Exclusive Pruning 258.97 183.23x 183x
BitSet 69.44 3.73x 683x
Vector 57.67 1.20x 823x
Mutable Vector 35.04 1.65x 1354x

The first improvement over the simple solution got us the most major speedup of 183x. After that, we followed the profiler, fixing bottlenecks by using the right data structures. We got quite significant speedup over the naive list-based solution, leading to drop in the run time from 259 seconds to 35 seconds. In total, we have done more than a thousand times improvement in the run time since the first solution!


In this post, we improved upon our list-based Sudoku solution from the last time. We profiled the code at each step, found the bottlenecks and fixed them by choosing the right data structure for the case. We ended up using BitSets and Vectors — both immutable and mutable varieties — for the different parts of the code. Finally, we sped up our program by 7.4 times. Can we go even faster? How about using all those other CPU cores which have been lying idle? Come back for the next post in this series where we’ll explore the parallel programming facilities in Haskell. The code till now is available here. Discuss this post on r/haskell or leave a comment.

  1. All the runs were done on my MacBook Pro from 2014 with 2.2 GHz Intel Core i7 CPU and 16 GB memory.

  2. A lot of the code in this post references the code from the previous posts, including showing diffs. So, please read the previous posts if you have not already done so.

  3. Notice the British English spelling of the word “Centre”. GHC was originally developed in University of Glasgow in Scotland.

  4. The code for the BitSet based implementa­tion can be found here.

  5. This article on School of Haskell goes into details about performance of vectors vs. lists. There are also these benchmarks for sequence data structures in Haskell: lists, vectors, seqs, etc.

  6. We see Haskell’s laziness at work here. In the code for the fixM function, the (==) function is nested inside the (>>=) function, but because of laziness, they are actually evaluated in the reverse order. The evaluation of parameters for the (==) function causes the (>>=) function to be evaluated.

  7. The code for the vector based implementa­tion can be found here.

  8. Unboxed vectors have some restrictions on the kind of values that can be put into them but Word16 already follows those restrictions so we are good.

  9. Haskell can be a pretty good imperative programming language using the ST monad. This article shows how to implement some algorithms which require mutable data structures in Haskell.

  10. The code for the mutable vector based implementation can be found here.

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Original post by Abhinav Sarkar - check out All posts on

Fast Sudoku Solver in Haskell #2: A 200x Faster Solution

In the first part of this series of posts, we wrote a simple Sudoku solver in Haskell. It used a constraint satisfaction algorithm with backtracking. The solution worked well but was very slow. In this post, we are going to improve it and make it fast.

This is the second post in a series of posts:

  1. Fast Sudoku Solver in Haskell #1: A Simple Solution
  2. Fast Sudoku Solver in Haskell #2: A 200x Faster Solution
  3. Fast Sudoku Solver in Haskell #3: Picking the Right Data Structures

Discuss this post on r/haskell.

Quick Recap

Sudoku is a number placement puzzle. It consists of a 9x9 grid which is to be filled with digits from 1 to 9 such that each row, each column and each of the nine 3x3 sub-grids contain all the digits. Some of the cells of the grid come pre-filled and the player has to fill the rest.

In the previous post, we implemented a simple Sudoku solver without paying much attention to its performance characteristics. We ran1 some of 17-clue puzzles2 through our program to see how fast it was:

$ head -n100 sudoku17.txt | time stack exec sudoku
... output omitted ...
      116.70 real       198.09 user        94.46 sys

So, it took about 117 seconds to solve one hundred puzzles. At this speed, it would take about 16 hours to solve all the 49151 puzzles contained in the file. This is way too slow. We need to find ways to make it faster. Let’s go back to the drawing board.

Constraints and Corollaries

In a Sudoku puzzle, we have a partially filled 9x9 grid which we have to fill completely while following the constraints of the game.

| . . . | . . . | . 1 . |
| 4 . . | . . . | . . . |
| . 2 . | . . . | . . . |
| . . . | . 5 . | 4 . 7 |
| . . 8 | . . . | 3 . . |
| . . 1 | . 9 . | . . . |
| 3 . . | 4 . . | 2 . . |
| . 5 . | 1 . . | . . . |
| . . . | 8 . 6 | . . . |
    A sample puzzle

| 6 9 3 | 7 8 4 | 5 1 2 |
| 4 8 7 | 5 1 2 | 9 3 6 |
| 1 2 5 | 9 6 3 | 8 7 4 |
| 9 3 2 | 6 5 1 | 4 8 7 |
| 5 6 8 | 2 4 7 | 3 9 1 |
| 7 4 1 | 3 9 8 | 6 2 5 |
| 3 1 9 | 4 7 5 | 2 6 8 |
| 8 5 6 | 1 2 9 | 7 4 3 |
| 2 7 4 | 8 3 6 | 1 5 9 |
    and its solution

Earlier, we followed a simple pruning algorithm which removed all the solved (or fixed) digits from neighbours of the fixed cells. We repeated the pruning till the fixed and non-fixed values in the grid stopped changing (or the grid settled). Here’s an example of a grid before pruning:

| [123456789] [123456789] [123456789] | [123456789] [123456789] [123456789] | [123456789] 1           [123456789] |
| 4           [123456789] [123456789] | [123456789] [123456789] [123456789] | [123456789] [123456789] [123456789] |
| [123456789] 2           [123456789] | [123456789] [123456789] [123456789] | [123456789] [123456789] [123456789] |
| [123456789] [123456789] [123456789] | [123456789] 5           [123456789] | 4           [123456789] 7           |
| [123456789] [123456789] 8           | [123456789] [123456789] [123456789] | 3           [123456789] [123456789] |
| [123456789] [123456789] 1           | [123456789] 9           [123456789] | [123456789] [123456789] [123456789] |
| 3           [123456789] [123456789] | 4           [123456789] [123456789] | 2           [123456789] [123456789] |
| [123456789] 5           [123456789] | 1           [123456789] [123456789] | [123456789] [123456789] [123456789] |
| [123456789] [123456789] [123456789] | 8           [123456789] 6           | [123456789] [123456789] [123456789] |

And here’s the same grid when it settles after repeated pruning:

| [    56789] [  3  6789] [  3 567 9] | [ 23 567 9] [ 234 6 8 ] [ 2345 789] | [    56789] 1           [ 23456 89] |
| 4           [1 3  6789] [  3 567 9] | [ 23 567 9] [123  6 8 ] [123 5 789] | [    56789] [ 23 56789] [ 23 56 89] |
| [1   56789] 2           [  3 567 9] | [  3 567 9] [1 34 6 8 ] [1 345 789] | [    56789] [  3456789] [  3456 89] |
| [ 2   6  9] [  3  6  9] [ 23  6  9] | [ 23  6   ] 5           [123    8 ] | 4           [ 2   6 89] 7           |
| [ 2  567 9] [   4 67 9] 8           | [ 2   67  ] [12 4 6   ] [12 4  7  ] | 3           [ 2  56  9] [12  56  9] |
| [ 2  567  ] [  34 67  ] 1           | [ 23  67  ] 9           [ 234  78 ] | [    56 8 ] [ 2  56 8 ] [ 2  56 8 ] |
| 3           [1    6 89] [     6  9] | 4           7           [    5   9] | 2           [    56 89] [1   56 89] |
| [ 2   6789] 5           [ 2 4 67 9] | 1           [ 23      ] [ 23     9] | [     6789] [  34 6789] [  34 6 89] |
| [12    7 9] [1  4  7 9] [ 2 4  7 9] | 8           [ 23      ] 6           | [1   5 7 9] [  345 7 9] [1 345   9] |

We see how the possibilities conflicting with the fixed values are removed. We also see how some of the non-fixed cells turn into fixed ones as all their other possible values get eliminated.

This simple strategy follows directly from the constraints of Sudoku. But, are there more complex strategies which are implied indirectly?

Singles, Twins and Triplets

Let’s have a look at this sample row captured from a solution in progress:

| 4           [ 2   6 89] 7           | 3           [ 2  56  9] [12  56  9] | [    56 8 ] [ 2  56 8 ] [ 2  56 8 ] |

Notice how the sixth cell is the only one with 1 as a possibility in it. It is obvious that we should fix the sixth cell to 1 as we cannot place 1 in any other cell in the row. Let’s call this the Singles3 scenario.

But, our current solution will not fix the sixth cell to 1 till one of these cases arise:

  1. all other possibilities of the cell are pruned away, or,
  2. the cell is chosen as pivot in the nextGrids function and 1 is chosen as the value to fix.

This may take very long and lead to a longer solution time. Let’s assume that we recognize the Singles scenario while pruning cells and fix the cell to 1 right then. That would cut down the search tree by a lot and make the solution much faster.

It turns out, we can generalize this pattern. Let’s check out this sample row from middle of a solution:

| [1  4    9] 3           [1  4567 9] | [1  4   89] [1  4 6 89] [1  4 6 89] | [1  4   89] 2           [1  456789] |

It is a bit difficult to notice with the naked eye but there’s something special here too. The digits 5 and 7 occur only in the third and the ninth cells. Though they are accompanied by other digits in those cells, they are not present in any other cells. This means, we can place 5 and 7 either in the third or the ninth cell and no other cells. This implies that we can prune the third and ninth cells to have only 5 and 7 like this:

| [1  4    9] 3           [    5 7  ] | [1  4   89] [1  4 6 89] [1  4 6 89] | [1  4   89] 2           [    5 7  ] |

This is the Twins scenario. As we can imagine, this pattern extends to groups of three digits and beyond. When three digits can be found only in three cells in a block, it is the Triplets scenario, as in the example below:

| [   45 7  ] [   45 7  ] [    5 7  ] | 2           [  3 5  89] 6           | 1           [  34   89] [  34   89] |

In this case, the triplet digits are 3, 8 and 9. And as before, we can prune the block by fixing these digits in their cells:

| [   45 7  ] [   45 7  ] [    5 7  ] | 2           [  3    89] 6           | 1           [  3    89] [  3    89] |

Let’s call these three scenarios Exclusives in general.

We can extend this to Quadruplets scenario and further. But such scenarios occur rarely in a 9x9 Sudoku puzzle. Trying to find them may end up being more computationally expensive than the benefit we may get in solution time speedup by finding them.

Now that we have discovered these new strategies to prune cells, let’s implement them in Haskell.

A Little Forward, a Little Backward

We can implement the three new strategies to prune cells as one function for each. However, we can actually implement all these strategies in a single function. But, this function is a bit more complex than the previous pruning function. So first, let’s try to understand its working using tables. Let’s take this sample row:

| [   4 6  9] 1           5           | [     6  9] 7           [ 23  6 89] | [     6  9] [ 23  6 89] [ 23  6 89] |

First, we make a table mapping the digits to the cells in which they occur, excluding the fixed cells:

Digit Cells
2 6, 8, 9
3 6, 8, 9
4 1
6 1, 4, 6, 7, 8, 9
8 6, 8, 9
9 1, 4, 6, 7, 8, 9

Then, we flip this table and collect all the digits that occur in the same set of cells:

Cells Digits
1 4
6, 8, 9 2, 3, 8
1, 4, 6, 7, 8, 9 6, 9

And finally, we remove the rows of the table in which the count of the cells is not the same as the count of the digits:

Cells Digits
1 4
6, 8, 9 2, 3, 8

Voilà! We have found a Single 4 and a set of Triplets 2, 3 and 8. You can go over the puzzle row and verify that this indeed is the case.

Translating this logic to Haskell is quite easy now:

We extract the isPossible function to the top level from the nextGrids function for reuse. Then we write the exclusivePossibilities function which finds the Exclusives in the input row. This function is written using the reverse application operator (&)4 instead of the usual ($) operator so that we can read it from top to bottom. We also show the intermediate values for a sample input after every step in the function chain.

The nub of the function lies in step 3 (pun intended). We do a nested fold over all the non-fixed cells and all the possible digits in them to compute the map5 which represents the first table. Thereafter, we filter the map to keep only the entries with length less than four (step 4). Then we flip it to create a new map which represents the second table (step 5). Finally, we filter the flipped map for the entries where the cell count is same as the digit count (step 6) to arrive at the final table. The step 7 just gets the values in the map which is the list of all the Exclusives in the input row.

Pruning the Cells, Exclusively

To start with, we extract some reusable code from the previous pruneCells function and rename it to pruneCellsByFixed:

Now we write the pruneCellsByExclusives function which uses the exclusivePossibilities function to prune the cells:

pruneCellsByExclusives works exactly as shown in the examples above. We first find the list of Exclusives in the given cells. If there are no Exclusives, there’s nothing to do and we just return the cells. If we find any Exclusives, we traverse the cells, pruning each cell to only the intersection of the possible digits in the cell and Exclusive digits. That’s it! We reuse the makeCell function to create a new cell with the intersection.

As the final step, we rewrite the pruneCells function by combining both the functions.

We have extracted fixM as a top level function from the pruneGrid function. Just like the pruneGrid' function, we need to use monadic bind (>>=) to chain the two pruning steps. We also use fixM to apply each step repeatedly till the pruned cells settle6.

No further code changes are required. It is time to check out the improvements.

Faster than a Speeding Bullet!

Let’s build the program and run the exact same number of puzzles as before:

$ head -n100 sudoku17.txt | time stack exec sudoku
... output omitted ...
      0.53 real         0.58 user         0.23 sys

Woah! It is way faster than before. Let’s solve all the puzzles now:

$ cat sudoku17.txt | time stack exec sudoku > /dev/null
      282.98 real       407.25 user       109.27 sys

So it is took about 283 seconds to solve all the 49151 puzzles. The speedup is about 200x7. That’s about 5.8 milliseconds per puzzle.

Let’s do a quick profiling to see where the time is going:

$ stack build --profile
$ head -n1000 sudoku17.txt | stack exec -- sudoku +RTS -p > /dev/null

This generates a file named with the profiling results. Here are the top five most time-taking functions (cleaned for brevity):

Cost Center Source %time %alloc
exclusivePossibilities (49,1)-(62,26) 17.6 11.4
pruneCellsByFixed.pruneCell (75,5)-(76,36) 16.9 30.8
exclusivePossibilities.\.\ 55:38-70 12.2 20.3
fixM.\ 13:27-65 10.0 0.0
== 15:56-57 7.2 0.0

Looking at the report, my guess is that a lot of time is going into list operations. Lists are known to be inefficient in Haskell so maybe we should switch to some other data structures?


As per the comment below by Chris Casinghino, I ran both the versions of code without the -threaded, -rtsopts and -with-rtsopts=-N options. The time for previous post’s code:

$ head -n100 sudoku17.txt | time stack exec sudoku
... output omitted ...
       96.54 real        95.90 user         0.66 sys

And the time for this post’s code:

$ cat sudoku17.txt | time stack exec sudoku > /dev/null
      258.97 real       257.34 user         1.52 sys

So, both the versions run about 10% faster without the threading options. I suspect this has something to do with GHC’s parallel GC as described in this post. So for now, I’ll keep threading disabled.


In this post, we improved upon our simple Sudoku solution from the last time. We discovered and implemented a new strategy to prune cells, and we achieved a 200x speedup. But profiling shows that we still have many possibilities for improvements. We’ll work on that and more in the upcoming posts in this series. The code till now is available here. Discuss this post on r/haskell or leave a comment.

  1. All the runs were done on my MacBook Pro from 2014 with 2.2 GHz Intel Core i7 CPU and 16 GB memory.

  2. At least 17 cells must be pre-filled in a Sudoku puzzle for it to have a unique solution. So 17-clue puzzles are the most difficult of all puzzles. This paper by McGuire, Tugemann and Civario gives the proof of the same.

  3. “Single” as in “Single child”

  4. Reverse application operation is not used much in Haskell. But it is the preferred way of function chaining in some other functional programming languages like Clojure, FSharp, and Elixir.

  5. We use Data.Map.Strict as the map imple­mentation.

  6. We need to run pruneCellsByFixed and pruneCellsByExclusives repeatedly using fixM because an unsettled row can lead to wrong solutions.

    Imagine a row which just got a 9 fixed because of pruneCellsByFixed. If we don’t run the function again, the row may be left with one non-fixed cell with a 9. When we run this row through pruneCellsByExclusives, it’ll consider the 9 in the non-fixed cell as a Single and fix it. This will lead to two 9s in the same row, causing the solution to fail.

  7. Speedup calculation: 116.7 / 100 * 49151 / 282.98 = 202.7

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Original post by Abhinav Sarkar - check out All posts on