Linear Interpolation

⎕io=0 assumed throughout; works in 1-origin with the obvious modifications.


On Wednesday, a question arrived via Dyalog Support from an intern in Africa: If M is the matrix on the left, use linear interpolation to compute the result on the right.

   1 20         1 20
   4 80         2 40
   6 82         3 60
                4 80
                5 81
                6 82

Linear Interpolation

Two points (x0,y0) and (x1,y1) specify a line; for any x there is a unique y on that line (assuming x0≠x1). The equation for the line derives as follows, starting from its slope m:

   m = (y1-y0) ÷ (x1-x0)
   (y-y0) = m × (x-x0)
   y = y0 + m × (x-x0)

Therefore, if is a 2-by-2 matrix of the two points and are the x-values to be interpolated, then:

   g ← {(⊃⌽⍺)+(⍵-⊃⍺)÷÷/-⌿⍺}

   ⊢ M←1 4 6,⍪20 80 82
1 20
4 80
6 82

   M[0 1;] g 2 3
40 60
   M[1 2;] g 5

A New Twist, A New Solution

The problem as posed implicitly required that:

  • The x-values are the positive integers bounded by ⊃⊖M.
  • Appropriate rows of the matrix are selected for a given x-value.
  • The missing x-values and their interpolations are “slotted back” into the argument matrix.

These requirements are best met by , interval index, a relatively new primitive function introduced in Dyalog APL version 16.0. The left argument must be sorted and partitions the universe into disjoint contiguous intervals; ⍺⍸⍵ finds the index of the interval which contains an item of . The result is ⎕io dependent.

For the given matrix M, the partition (of the real numbers in this case) is depicted below. As in conventional mathematical notation, [ denotes that the interval includes the left end-point and ) denotes that the interval excludes the right end-point.

          1        4      6
     ¯1       0       1       2

   v←¯5 0 1 2.5 6 3 4 5 9 8 7

   1 4 6 ⍸ v
¯1 ¯1 0 0 2 0 1 1 2 2 2

   v ,[¯0.5] 1 4 6 ⍸ v
¯5  0 1 2.5 6 3 4 5 9 8 7
¯1 ¯1 0 0   2 0 1 1 2 2 2

With in hand, the problem can be solved as follows:

  (x y)←↓⍉⍵

   interpol M
1 20
2 40
3 60
4 80
5 81
6 82

The problem of x-values less than the first end-point is finessed by applying 0⌈ to the interval indices, and that of x-values greater than or equal to the last end-point is finessed by repeating the last slope m←m,⊃⌽m.

It is possible to do the interpolation only on the missing indices (2 3 5 in this case) and insert them into the argument matrix. It seems neater to simply interpolate everything, and in so doing provide a check that the interpolated values equal the values given in the argument.

An Alternative Interpolation

Interpolating according to two selected rows of a matrix of points treats the function as piecewise linear, with sharp inflection points where the lines join (different slopes between adjacent lines). A “holistic” alternative approach is possible: the matrix can be interpreted as specifying a single line and the interpolation is according to this single line. The primitive function computes the coefficients of the line which best fits the points:

   ⎕rl←7*5  ⍝ for reproducible random numbers

   ⊢ M←t,⍪(?7⍴5)+¯17+3×t←?7⍴100
35  89
98 278
19  44
 4  ¯5
62 170
49 133
25  59

   M[;1] ⌹ 1,M[;,0]    ⍝ y-intercept and slope
¯15.3164 2.99731

   interpola ← {(1,⍤0⊢⍵)+.×⍺[;1]⌹1,⍺[;,0]}

   M[;1] ,[¯0.5] M interpola M[;0]
89      278    44      ¯5       170     133     59     
89.5895 278.42 41.6325 ¯3.32713 170.517 131.552 59.6164

   M interpola 33 35 37 39.7
83.5949 89.5895 95.5841 103.677


Our best wishes to the intern. Welcome to APL!

Phil Goacher (05-11-40 – 09-03-18)

Phil Goacher
I attended Phil Goacher’s funeral yesterday. He probably had no idea of just how much he would influence the development of APL. I first met Phil at W S Atkins which is a large UK engineering consultancy. There was a sub unit called Atkins Computing which provided internal computing support to the engineers and, crucially, provided a time sharing service to external users. He became my manager when I moved from supporting the transportation program suite to the brand new APL team.

Phil was more manager and business man than programmer but he had an engineering background in Gas distribution and thus the mathematical bias that seems common to APL users.

Phil was the sort of man with an eye for a business opportunity and when a advert appeared from someone who wanted to set up an APL consultancy with a limited objective to provide resources to Rank Xerox (UK arm of Xerox Corp) he turned up along with half his team. Phil was a wheeler dealer type and had already talked to the recruiter, persuaded him that he could manage the team, and then interviewed the candidates. So I wound up being interviewed by my existing manager. Following this Phil decided we only needed a salesman and we could set the consultancy up with a much wider remit. Phil recruited Ted Hare who was one of Atkin’s top salesmen and Dyadic Systems was born.

Phil did all of the necessary legal and administration to get Dyadic off the ground and became the company’s administration guy. He bought an Apple 2 with VisiCalc so was early into the spreadsheet concept. It must be said that all five of the people who started Dyadic were “early adopters” by nature.

Dyadic was quite successful as an APL consultancy but wanted to do more. I am not sure who instigated it (I was too busy consulting) but it was probably Phil’s idea to develop an APL of our own. Knowing Phil this was probably a business eye and a request from Pam Geisler who was Pauline Brand’s sister (Pauline was an early recruit that we “acquired” from Atkin’s) but, more importantly, high up in the management of Zilog. Zilog was developing a new 16 bit chip, Z8000, that was going to push them up market from the 8-bit chip with which they had had enormous success – Z80. IBM had been pushing APL as the future, and it was incumbent on competitors to provide an APL offering.

Phil and Dave Crossley will have been the instigators of recruiting John Scholes, who had also been at Atkin’s, and teaming him with me to develop Dyalog (Dyadic + Zilog). Dave provided the management overview of that project while Phil and Ted had to worry about financing John and I, who were not bringing in consultancy money. It was at this point that Dyalog APL became a UNIX and C project. It was much later when I read Brian Kernighan’s obituary that I realised just how early we had got into UNIX and C.

Dyalog APL was not the instant success that Dyadic hoped for. It had increased costs. We needed an office and the paraphernalia that goes with selling a product rather than brains. Dyadic ran into financial issues and was taken over by Lynwood Scientific, who made terminals with Zilog Z8000 chips inside. I think they really wanted the Z8000 expertise that John and I had acquired. As part of that takeover Phil, Ted and Dave had to leave with a derisory pay-off and I lost touch until I learnt of his death a fortnight ago.

From the perspective of APL language development Phil is not a tower of strength. However, from the perspective of having provided the impetus and drive to get Dyalog APL developed, he is very important.

NOTE: For anyone interested in reading more about the early days of APL, see the Vector special on the first 25 years of Dyalog Ltd.

Permuting Internal Letters

Friday Afternoon

It’s something of a custom in Dyalog to send a “fun” e-mail to the group on Friday afternoons. My gambit for this past Friday was:

   x ←' according to research it doesn''t matter'
   x,←' what order the letters in a word are'
   x,←' the human mind can still read it'
   x,←' the only important thing is that'
   x,←' the first and the last letters are in the right place'

   ∊ ' ',¨ {(1↑⍵),({⍵[?⍨≢⍵]}1↓¯1↓⍵),(-1<≢⍵)↑⍵}¨ (' '∘≠ ⊆ ⊢) x
 aroidnccg to reasrech it dneso't mttear waht order the ltreets 
      in a wrod are the hmaun mnid can sltil read it the olny 
      imartpnot tihng is that the fsrit and the lsat lterets 
      are in the rhigt palce

(The research:

Code Golfing

The code golfers were not long in responding:

   ∊{' ',⍵[1,(1+?⍨0⌈¯2+n),n/⍨1<n←≢⍵]}¨(' '∘≠⊆⊢)x     ⍝ fa
   ∊{' ',⍵[∪1,{⍵,⍨?⍨⍵-1}≢⍵]}¨(' '∘≠⊆⊢)x              ⍝ fb
   '\S+'⎕r{{⍵[∪1,{⍵,⍨?⍨⍵-1}≢⍵]}⍵.Match}x             ⍝ fc
   ∊{' ',⍵[n↑1,(1+?⍨0⌈¯2+n),n←≢⍵]}¨(' '∘≠⊆⊢)x        ⍝ fd

In defense of the original expression, it can be said that it follows the pattern:

  • cut into words            (' '∘≠ ⊆ ⊢) x  also ' '(≠⊆⊢)x
  • permute each word      {(1↑⍵),({⍵[?⍨≢⍵]}1↓¯1↓⍵),(-1<≢⍵)↑⍵}¨
  • undo the cut             ∊ ' ',¨

That is, the pattern is “permute under cut”. Moving the ' ', into the permuting function, while shortening the overall expression, obscures this pattern.

Golfing for Speed

One of the code golfers was undaunted by the highfalutin’ functional programming argument. Changing tack, he claimed to be golfing for speed (while himself travelling at 900 kph):

  p←1+0,⍸⍵=' '           ⍝ start of each word
  n←0⌈¯3+¯2-/p,2+≢⍵      ⍝ sizes of windows to be shuffled
  ⍵[∊p+?⍨¨n]@((⍳¯1+≢⍵)~p∘.-0 1 2)⊢⍵

   cmpx 'fe x' 'fb x' 'fc x' 'fd x'
  fe x → 4.27E¯5 |    0% ⎕⎕⎕⎕
* fb x → 1.05E¯4 | +145% ⎕⎕⎕⎕⎕⎕⎕⎕⎕
* fc x → 3.39E¯4 | +692% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕
* fd x → 1.11E¯4 | +159% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕

The question was then posed whether the algorithm can be “flattened” further; that is, whether the expression ∊p+?⍨¨n in function fe can be done without each. The answer is of course yes (else I wouldn’t be writing this blog post :-). Flattening, or avoiding the creation of nested arrays, has the potential to reduce memory consumption and increase efficiency, because there is more potential for the interpreter to perform optimized sequential or even parallel operations.

Partitioned Random Permutations

In the old days, before general arrays, before the each and rank operators, ingenious techniques were devised for working with “partitioned” arrays: A boolean vector with a leading 1 specifies a partition on a corresponding array with the same length, basically what we can now do with or similar facility. A detailed description can be found in Bob Smith’s APL79 paper A Programming Technique for Non-Rectangular Data.

   p←1 0 0 1 1 0 0 0 0 0
   v←3 1 4 1 5 9 2 6 53 58

│3 1 4│1│5 9 2 6 53 58│

   +/¨ p⊂v
8 1 133
8 1 133

   ∊ +\¨ p⊂v
3 4 8 1 5 14 16 22 75 133
3 4 8 1 5 14 16 22 75 133

The last two sets of expressions illustrate how “partitioned plus reduce” and “partitioned plus scan” can be computed, without use of general arrays and without each.

At issue is how to do “partitioned random permute”. Answer: ⍋(n?n)+n×+\p ⊣ n←≢p.

p                1  0  0    1    1  0  0  0  0  0
n?n              5  4 10    6    2  9  8  1  3  7
n×+\p           10 10 10   20   30 30 30 30 30 30
(n?n)+n×+\p     15 14 20   26   32 39 38 31 33 37
⍋(n?n)+n×+\p     2  1  3    4    8  5  9 10  7  6


  b←~(⊢ ∨ 1∘⌽ ∨ ¯1∘⌽)' '=⍵  ⍝ internal letters
  n←≢p←b/b>¯1⌽b             ⍝ partition vector for groups of same

   cmpx 'ff x' 'fe x'
  ff x → 1.46E¯5 |    0% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕
* fe x → 4.19E¯5 | +187% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕


   cmpx 'ff x6' 'fe x6'
  ff x6 → 5.16E¯2 |    0% ⎕⎕⎕⎕⎕⎕⎕⎕⎕
* fe x6 → 1.77E¯1 | +243% ⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕⎕

There is a puzzle from 1980 which similarly involved randomly permuting items within groups. See A History of APL in 50 Functions, Chapter 47, Pyramigram. The puzzle solution is amusing also for that fact that it used -\.


Plobssiy, a txet wshoe words are ilivuddinaly ptemrued is rdeaable mnaliy bseacue in odirrany txet many wrods, eelpclsaiy iptnroamt wrods, are sorht. A curops wtih long and ufnmiailar wdors wuold lkiely be hard to raed atfer scuh pittarmoeun. For eapxmle:

   ff t
 eyonelsmeary dpihnoeissopt siiuqpeedlasan pniormoasaac oopimoentaoa
   ff t
 emlresaneyoy dnpepoihissot seilesiqaadupn poimrnaaasoc oaioomtpneoa
   ff t
 erloymneseay dhsiepopiosnt seildspqiauean praisoaaonmc oomatneiopoa

Stackless Traversal

Enlist (∊) is twice as fast in Dyalog 16.0 as it was in Dyalog 15.0. Pretty much across the board: ∊⍳100 is not going to be any faster, but whenever the argument is a nested array and the simple arrays it contains are reasonably small, there are huge performance improvements. How did we achieve the huge speedup?


The usual way for a C programmer to write the traversal used in Enlist would be a simple recursive function: If the current array is simple, handle it, and if it is nested, traverse on each array it contains. We can’t do this, though, because it would break our product. The specific problem is that the C stack used for function recursion has a limited (and fairly small) size, while the depth of nesting in an array is limited only by available memory. When passed a tree a few thousand layers deep, an Enlist implemented using the C stack would run out of space, then attempt to write past the end of the stack, resulting in a segfault and a syserror 999.
So keeping memory on the stack is out. The only place we can store an arbitrary amount of memory is the workspace itself. But storing things in the workspace is harder than it sounds. When Dyalog fails to do a memory allocation, it tries to get more space by compacting pockets of memory and squeezing arrays. This happens rarely, but any operation that allocates memory has to take it into account. That means it has to register all of the memory it uses so it can find it again when memory is shuffled around.

The old approach: trav()

Dyalog has a general function for traversing arrays, which will take care of all of the problems above. It’s very flexible, but in enlist it’s used for a single purpose: to call a function on each simple array (“leaf”) in a nested array. The old Enlist used it twice, first to calculate the type and length of the final result, and again to move all of the elements into a result array after it’s allocated.
Trav works very well, but is also very slow. It stores a lot of information, and constantly allocates memory to do so. It also is much more general than it needs to be for enlist, so it spends a lot of time checking for options we never use. We can do better.

A different strategy

Rather than allocating a bunch of APL pockets to emulate the C stack, what if we just didn’t use the stack at all? Sounds kind of difficult, since we definitely need some sort of stack to remember where we are during traversal. Once we finish counting or copying the last leaf in a branch, we have to find our way back up to the pocket we started at. This pocket could be anywhere from one to a billion levels above us, and we have to remember the location of every pocket in between. And once we return to a pocket, we have to remember how many children we have already traversed, so we can move on to the next one. Where do we store all of this information?

Fun with pointers

Suppose we are in the middle of traversing a nested array p. An array in Dyalog is a pointer to a pocket of memory, which contains a header followed by some other pocket pointers p[0], p[1], and so on. We consider the pocket pointers themselves, rather than the data that they point to, to be arrays since pointers can be stored and manipulated directly and pockets cannot. If the rank of p is more than one, p[i] refers to the i’th array in ,p.
Let’s assume we have gone through the first array p[0] in p and now want to begin traversing p[1], which we rename q. In order to move on to p[2] after we finish with q we will need some stored information—a stack-based algorithm might push p and the current index 1 onto the stack. A variable-size stack is needed rather than some constant amount of memory because this information has to be stored at every level of the arbitrarily-deep traversal. We don’t want to use such a stack, but there is a redundant value we can use: the pointer p[1]=q stored in p. When we finish traversing q and come back to p, we know the address of q, since we just traversed it! So we can safely overwrite that address. But all of the other nearby pointers are in use. It would be very difficult to find space for another word. So instead of storing the location of p, we store a slightly different address: ptr, which points to p[1] (we’ll discuss how to recover the pointer p from ptr later). It won’t help to store ptr in p, since it would just point to its own location! Instead we use a temporary variable prev to hold onto it while we traverse q, and store the previous value of prev, which points to a location inside p’s parent, in the location that used to hold q. By the time we begin traversing q’s first child, r, the picture looks like this:
We have reversed all of the pointers above us, and now we have a trail leading all the way back up to the array we started at. To do this we needed an extra temporary value, prev. This uses more memory, but only a constant amount (one word), so it won’t cause problems for us. However, it might be helpful to consider why we need that extra word. In a stack-based traversal only one temporary pointer would be needed to iterate over the array, and in fact we do get away with only writing one word of data in each array during traversal. But the first array doesn’t have any parent array, so there is no pointer to write in it. Instead we write a zero (which is not a valid pointer) to indicate that fact. This zero is an extra word that doesn’t correspond to any array, so to compensate for it we use an extra temporary variable.

When to stop

When we finish traversing a pocket, we end up with another problem. We don’t know we’re done! Following the algorithm as described so far, we would end up with ptr pointing at the last element of q, which looks like all of the others, and we would keep moving past the end of q, landing on the header of the next pocket, which probably isn’t even a pointer. Segfault—do not pass go; do not collect the elements of the original array into a list.

A typical algorithm would just store the length of the array on the stack, which we can’t do. However, there is a little more space hiding in the array q which we can use. A pointer gives the location of a particular byte in memory. But the pointers in q all point to the beginning of pockets, which are word-aligned: they begin at addresses which are all multiples of 4 bytes (in 32-bit builds) or 8 bytes (64-bit builds). So we have at least two bits at the bottom of each pointer which are guaranteed to be zero. As long as we remember to clear those bits once we finish, and before following a pointer, we can write anything we want in them!
The first thing to write is a stop bit. We mark the last pointer q with some two bits which aren’t zero (we’ll decide what later). While shuffling pointers around, we always leave the bottom two bits where they were, so that when we get back to q[n] we see the two bits we wrote when we started traversing q. If we haven’t reached the end, that could be anything other than the stop code, and if we have reached the end, it must be the stop code. So now we know when to stop, and won’t segfault by running past the end of a pocket.
However, in order to get back to the parent array with all of our state intact (specifically, the overwritten pointer to the current array), we need to know the address of the beginning of our pocket, not the end. And the shape of the pocket, which we would need to find the pocket’s length, is kept at the beginning of the array as well. How do we get back to the start?

Retracing our steps

Obviously we need to start moving backwards. But once again, we need to know when to stop. Since we’re about to run into it face-first, maybe we should discuss what comes before the pointers in a pocket?

The contents of an array pocket are pretty simple. There are three words at the beginning: the length and reference count of the pocket (which we don’t need to worry about), and the zones field, which contains the type and rank of the pocket along with potentially some other data. Next is the shape (which might be empty!), and then the data in the array—for a nested pocket, pointers to other pockets. There are three cases we want to consider:

  1. The shape is empty, which means q contains only one pointer. We will run into the zones field right before the last (and first) pointer.
  2. The shape is not empty, but the total number of pointers is small. We will run into the shape after a short amount of backwards searching.
  3. The total number of pointers is large. It would take a while to get back to the beginning one pointer at a time, but we also have a lot of spare bits at the bottom of those pointers…
  4. (Extra credit) The pocket is empty, but it still has a prototype to traverse. For enlist we ignore these pockets, but for other purposes we have to handle this case. The shape has to have a zero in it, but the other axes could have any size that fits in a word.

For the first two cases, we mark the shape and the zones field, respectively. By a lucky coincidence, the bottom two bits of the zones field are used to store the type of the pocket we are in, and for nested arrays, the two bits we care about are always both 1. So let’s make 3 (in binary, 11) the indicator for the zones field. In case 2, the last item of the shape is small, so there are free bits at the top. We’ll shift the shape up to move those to the bottom, leaving room to store the rank and a marker. We’ll choose 1 for the marker.
In case 3, there are enough pointers to store the length of the array in the bottom bits. Since we can’t use the stop marker to encode the length, we opt to just use the bottom bit, that is, storing zeros and ones in the bottom two bits, of each pointer. To distinguish from case 2 we mark the beginning of this offset with a 2, the only unused code so far. We mark the end of the offset with a 2 as well, and then to read the offset we just move backwards, adding each bit to the end of an integer, until we reach that 2. Then we use the length to get back to the beginning of an array. While moving backwards, we also clear those bits to leave the pointers pointing at the exact, byte aligned start of pockets like they did when we found them.
Case 4 is best left as an exercise, I think. There’s a whole empty word in the shape for you to use—what could be easier? Granted, it’s stashed away in some random location in the shape, but that shouldn’t be an obstacle.

Conditions of use

The traversal described above is much faster than trav, but it requires more care to use. Since it scribbles notes on parts of arrays during traversal, the workspace isn’t necessarily in a valid state until it finishes. So it’s not safe to call memory management functions during traversal. Any memory used needs to be allocated before the start of traversal, which means it has to have a constant size: we can’t use anything that will grow as needed.
Enlist is made up of two very simple functions which meet this requirements in most cases: the first goes through and records what types are in the array and how many total elements there are, and the second actually moves the elements into a result array. For the counting function, we just need a little extra storage to fit the type and the count. That’s a constant amount, so it’s fine. For the moving function, we can’t promote an element of a non-mixed array to an element of a mixed array, since this requires us to allocate a new array for it. But this is not a very common case, and when it does happen it will be slow anyway. This means it’s okay to use trav for that case, and use the faster traversal otherwise.


Here are timings showing how long it takes to enlist various arrays, and the improvement from version 15.0 to version 16.0. Timings are measured on a modern CPU, an Intel Kaby Lake i7, but I don’t expect the ratios to change very much on other machines.

APL expression Description 15.0 16.0 Ratio
{(?⍵⍴10){⊂⍵}⌸?⍵⍴99} 1e3 1e3 bytes in 10 groups 6.138E¯7 2.898E¯7 2.118
{(?⍵⍴10){⊂⍵}⌸?⍵⍴99} 1e4 1e4 bytes in 10 groups 1.174E¯6 9.439E¯7 1.244
{(?⍵⍴10){⊂⍵}⌸?⍵⍴99} 1e5 1e5 bytes in 10 groups 8.799E¯6 8.168E¯6 1.077
{⍵⊂⍨1,1↓1=?(≢⍵)⍴2}⍣10?1e4⍴99 1e4 bytes nested 10 deep 8.540E¯4 3.464E¯4 2.466
⊂⍣1e3⊢2 3 A 1000-deep nesting 1.032E¯4 1.533E¯5 6.730
,⍨∘⊂⍣20⊢2 3 Duplicated references 1.648E¯1 3.595E¯2 4.584

The first row is probably the most typical case: a single array with a little bit of nesting, but fairly small leaves. In this case, we can expect to see about a factor of two improvement. As the leaves grow larger, the running time becomes dominated by the time to actually move data to the result, and the improvement falls off, dropping to 25% around 1000 bytes per leaf and 0.8% at 1e4 bytes per leaf. The new code is faster even at very small numbers of leaves. In fact, it takes less time to start up an array traversal than the old method, so it’s many times faster on very deeply nested arrays with few elements in each array. We can see from the final row that the new method has no issues when it encounters the same array many times in one traversal.
The same algorithm is also used for hashing arrays in Dyalog 16.0, which in turn is used for dyadic iota and related functions. The impact on individual operations is not quite as much as the improvements to enlist shown above, but they are still substantial, and being able to search for a few strings in a list more quickly will surely help a lot of applications.

Dyalog ’17: Day 2 (Monday 11 September)

by Vibeke Ulmann

Focus on Dyalog APL the language – Monday 11th September 2017

Where Sunday is traditionally filled with workshops and hands-on experiences – the first proper day of the annual user meeting is Monday – and this year was no different.

CEO, Gitte Christensen opened the meeting and emphasised a few of the major new things that have been achieved since last year, namely:

  • RIDE Version 4
  • Embedded HMTL Rendering Engine and – as always –
  • Performance enhancements.

She highlighted the fact that Version 16.0 was the beginning of a tool chain for developing distributed applications – including Cloud computing.

The licence for the SyncFusion library has been renewed for another 5 years. So, for those working with SyncFusion, you will have the usual widgets for dashboarding and graphing to hand.

A new multi-platform developer licence is now available allowing for development on all platforms; Windows, Linux, Mac, and soon, Android.

The Tools Group has been expanded, and they are producing more and more examples and templates.

Dyalog is now producing live content (outside of the user meeting) in the form of webcasts – currently one a month – and Podcasts are also planned.

CXO Morten Kromberg gave us a look at the next generation APL.

Authors note: if you are wondering what the X in CXO stands for it is ‘Experience’.

Morten established that there is a general ‘climate change’ in the world of computing especially as cloud computing is now ‘THERE’. This means that performance once again becomes key, as the true cost of cloud computing is measured in Watts – meaning CPU and memory consumption. So, if you can reduce the footprint of your application, you can reduce the costs of cloud hosting. Another point made is that Cloud computing generally means Linux – as it uses less memory and, therefore, fewer Watts. Whereas macOS and Androids can be considered UNIXes.

Morten focused on the demand for a new generation of APL developers and more to the point, managers who are comfortable using APL, and APL programmers. There are a number of criteria that both groups need; the developer need a modern set of libraries to build upon and to be able to find them easily for example using Git. Whereas managers need test driven development, source code management, and continuous development cycles.

Not everyone is a familiar with Git, and some are even a tad intimidated by it. But there is much good to be said for Git. You can have a private area, as well as a public shared area. Dyalog currently has 25 public repositories on Git. More will follow over time.

CTO Jay Foad proceeded to outline how we can make some of the Dyalog dreams for the future come true.

Version 16.0 of Dyalog APL was released in June this year, and work on version 17.0 continues apace. Speculatively Jay highlighted some of the key areas in version 17.0 to be: scripting, language, performance, and bridges to Python, Julia, MATLAB and Haskell. More work on RIDE, GPUs (and Xion Phi), portability and Android, the Cloud, shuffle testing and PQA.

The Key Note speech before Lunch was presented by Aaron Hsu from Indiana University (USA). Aaron went through how you can escape the beginner’s plateau when starting to work with APL.

The key takeaways for yours truly was that Aaron has observed two attitudes to APL

1)     Never in my life

2)     Can’t imagine life without it

He also observed that there seems to be a ‘learning wall’ which we need to find a way to overcome.

A directory of best practices can give insight into why computer scientists, or those trained in traditional programming methods, often find APL jarring and difficult, whereas those with no prior training fall in love with APL and take to it like ducks to water.

Watch his presentation in which he walks through 8 patterns, he considers to be key for newbie APL programmers. We will announce when the presentation is available on Dyalog’s YouTube Channel later in the autumn.

After lunch, most of the afternoon was dedicated to looking deeper into some of the new feature/functionality and topics many APL programmers find of particular interest.

In the interest of enticing you to watch the presentations online when they’re posted on Dyalog’s YouTube Channel, this blog only touches the basics on a couple of the presentations.

John Scholes went through re-coding from procedural to denotative style and showed us how pure functions opens for code reduction when implemented. ‘Massaging’ the code was the new expression I came away with.

Roger Hui showed us how he has now managed to solve a 20-year-old problem: ‘Index-of’ on Multiple Floats. After having initially established – to much hilarity – that the best way of solving the problem at hand was to not introduce it in the first place in your code, Roger proceeded to show how it can now be solved. Intentionally I am not giving away what Rogers 20-year-old ‘Problem’ was. Let me just briefly mention: it has to do with X and Y.

The afternoon was rounded off with a user presentation by Kostas Blekos from the University of Patras (Greece), where a group of physicists have used APL for the research they did for a paper.

His initial premise was that Physicists + Programming = Disaster. On the other hand, physicists need to do a lot of programming, so when they were developing the basis for the paper, they wanted to find a (new) language that made it easier to do better (and faster) prototyping.

Lots of Kostas’ and his colleagues’ previous work had been done in FORTRAN and, as he said, we needed something a bit easier to work with and the choice was Dyalog APL. Outside of the ability to do fast prototyping, the terseness of the language was attractive, as were the close mathematical relations, which made it easy to understand.

What they learned was that APL is GREAT, suitable for fast prototyping, and for avoiding making mistakes. The quote of the day surely must be

In FORTRAN I could spend a whole day trying to find a missing comma……..

Asked if there were any downsides to APL, Kostas said no, not really, except it is difficult to convince people to use it.

Dyalog ’17: Day 1 (Sunday 10 September)

by Vibeke Ulmann

SharpPlot and SharpLeaf – the graphics and publication tools included with Dyalog on all platforms

(Workshop SA4)

A lot of work has gone into SharpPlot and SharpLeaf over the past couple of years*. The workshop on Sunday 10th September was run by Nicolas Delcros, the Dyalog software programmer behind the work.


The documentation has been significantly improved, comes across as comprehensive, and is available on right-click anywhere when you use the tool. It is also available on and can be downloaded as a PDF. Nic strongly recommends that you walk through the documentation and tutorials and familiarise yourself with the capabilities and the different graph types that are available for automatic graphical representation of data before you start using SharpPlot. That way you will save yourself a lot of time when you start working with the tool, and you will also quickly discover which types of graphs are ‘best’ to show your particular data.

It’s possible to click on the graph widget within a Dyalog Session to immediately see how the data displays graphically. If it doesn’t work to your satisfaction, or clearly show the data the way you want in one type of graph, you can right-click on the data and choose the graph wizard.

Output can be Raster or Vector format, and you can publish to the Web or UI (User Interface).

Chart types are 2D and 3D. There are also a number of miscellaneous charts such as Gantt, Triangle, Table and Venn diagrams.

There are obviously a lot of options you can tweak for each graph and the graph wizard presents them to you and lets you see the effects immediately. Once you are happy with the graph you can turn the specs into a script which can be incorporated in your program.

All in all, SharpPlot now comes across as highly intuitive and easy to use.

We recommend you review the blogpost on from April 2017 where Nic explains some of the concepts further:


SharpLeaf is an automated publication tool, which comes in extremely handy if, for example, you have to create the same report on a regular basis, but have to use/show different data from the previous report. A good example could be Board reports with varying data from Finance and/or Product Sales.

A lot of people will be pleased to find that their standard multi page report – which hitherto they have had to develop for example in Word and Excel each month – can now be generated automatically, with changes made to the embedded graphs and tables, based on data accrued since the previous month.

If you want this level of automation, you will have need to swap to SharpLeaf, develop your standard report, and then carry on from there. Importing an old report developed in, for example, Word or Excel is, unfortunately, not an option (but cutting and pasting text is).

* Author’s note: The original software programming work on SharpPlot and SharpLeaf was done by Causeway. Causeway was acquired by Dyalog Ltd in May 2007.