Smallish edits

1 files changed, 9 insertions, 7 deletions

diff --git a/implementation/kclaims.md b/implementation/kclaims.md
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+++ b/implementation/kclaims.md
@@ -6,9 +6,9 @@ Sometimes I see unsourced, unclear, vaguely mystical claims about K being the fa
 
 This isn't meant to put down the K language! K is in fact the only APL-family language other than BQN that I would recommend without reservations. And there's nothing wrong with the K community as a whole. Go to [the k tree](https://chat.stackexchange.com/rooms/90748/the-k-tree) and meet them! What I want to fight is the *myth* of K, which is carried around as much by those who used K once upon a time, and no longer have any connection to it, as by active users.
 
-One theme I find is equivocating between different kinds of performance. I suspect that for interpreting scalar code K is faster than APL and J but slower than Javascript, and certainly any compiled language. For operations on arrays, maybe it beats Javascript and Java but loses to current Dyalog and tensor frameworks. Simple database queries, Shakti says it's faster than Spark and Postgres but is silent about newer in-memory databases. The most extreme K advocates sweep away all this complexity by comparing K to weaker contenders in each category. Just about any language can be "the best" with this approach.
+The points I argue here are narrow. To some extent I'm picking out the craziest things said about K to argue against. Please don't assume whoever you're talking to thinks these crazy things about K just because I wrote them here. Or, if they are wrong about these topics, that they're wrong about everything. Performance is a complicated and often counter-intuitive field and it's easy to be mislead.
 
-I've had to expand on things here, inferring details because I haven't been able to get K supporters to articulate their points. If you know something I don't, I'd be interested in discussing it. If you can offer concrete evidence against my arguments below, I promise I'll revise this page to include it, even if I just have to quote verbatim because I don't understand a word of it.
+On that note, it's possible *I've* made mistakes, such as incorrectly designing or interpreting benchmarks. If you present me with concrete evidence against something I wrote below, I promise I'll revise this page to include it, even if I just have to quote verbatim because I don't understand a word of it.
 
 ## The fastest array language
 
@@ -18,7 +18,9 @@ The reason I have no measurements is that every contract for a commercial K incl
 
 The primary reason I don't give any credence to claims that K is the best is that they are always devoid of specifics. Most importantly, the same assertion is made across decades even though performance in J, Dyalog, and NumPy has improved by leaps and bounds in the meantime—I participated in advances of [26%](https://www.dyalog.com/dyalog/dyalog-versions/170/performance.htm) and [10%](https://www.dyalog.com/dyalog-versions/180/performance.htm) in overall Dyalog benchmarks in the last two major versions. Has K4 (the engine behind kdb and Q) kept pace? Maybe it's fallen behind since Arthur left but Shakti K is better? Which other array languages has the poster used? Doesn't matter—*they* are all the same but *K* is better.
 
-It's also crucial to know what kind of code is being run. Below I discuss array-based versus scalar code, but here's another case. It's well known that K works on one list at a time, that is, if you have a matrix—in K, a list of lists—then applying an operation (say sum) to each row works on each one independently. If the rows are short then there's function overhead for each one. In APL, J, and BQN, the matrix is stored as one unit with a stride. The sum can use one metadata computation for all rows, and there's usually special code for many row-wise functions. I measured that Dyalog is 30 times faster than ngn/k to sum rows of a ten-million by three float (double) matrix, for one fairly representative example. It's fine to say—as many K-ers do—that these cases don't matter or can be avoided in practice; it's dishonest (or ignorant) to claim they don't exist.
+A related theme I find is equivocating between different kinds of performance. I suspect that for interpreting scalar code K is faster than APL and J but slower than Javascript, and certainly any compiled language. For operations on arrays, maybe it beats Javascript and Java but loses to current Dyalog and tensor frameworks. Simple database queries, Shakti says it's faster than Spark and Postgres but is silent about newer in-memory databases. The most extreme K advocates sweep away all this complexity by comparing K to weaker contenders in each category. Just about any language can be "the best" with this approach.
+
+Before getting into array-based versus scalar code, but here's a simpler case. It's well known that K works on one list at a time, that is, if you have a matrix—in K, a list of lists—then applying an operation (say sum) to each row works on each one independently. If the rows are short then there's function overhead for each one. In APL, J, and BQN, the matrix is stored as one unit with a stride. The sum can use one metadata computation for all rows, and there's usually special code for many row-wise functions. I measured that Dyalog is 30 times faster than ngn/k to sum rows of a ten-million by three float (double) matrix, for one fairly representative example. It's fine to say—as many K-ers do—that these cases don't matter or can be avoided in practice; it's dishonest (or ignorant) to claim they don't exist.
 
 ## Scalar versus array code
 
@@ -28,15 +30,15 @@ Popular APL and J implementations interpret source code directly, without even b
 
 K is designed to be as fast as possible when interpreting scalar code, for example using a [grammar](https://k.miraheze.org/wiki/Grammar) that's much simpler than [BQN's](../spec/grammar.md) (speed isn't the only benefit of being simpler of course, but it's clearly a consideration). It succeeds at this, and K interpreters are very fast, even without bytecode compilation in advance.
 
-But K isn't good at scalar code either! It's also an interpreter for a dynamically-typed language, and will be slower than compiled languages like C and Go, or JIT-compiled ones like Javascript and Java. A compiler generates code to do what you want, while an interpreter is code that reads data (the program) to do what you want. This is why BQN uses compiler-based strategies to speed up execution, first compiling to bytecode to make syntax overhead irrelevant and then usually post-processing that bytecode.
+But K still isn't good at scalar code! It's an interpreter (if a good one) for a dynamically-typed language, and will be slower than compiled languages like C and Go, or JIT-compiled ones like Javascript and Java. A compiler generates code to do what you want, while an interpreter is code that reads data (the program) to do what you want. Once the code is compiled, the interpreter has an extra step and *has* to be slower. This is why BQN uses compiler-based strategies to speed up execution, first compiling to bytecode to make syntax overhead irrelevant and then usually post-processing that bytecode. Compilation is fast enough that it's perfectly fine to compile code every time it's run.
 
 ## Instruction cache
 
-A more specific claim about K is that the key to its speed is that the interpreter, or some part of it, fits in L1 cache. I know Arthur Whitney himself has said this but can't track down the source now. Maybe this was a relevant factor in the early days of K around 2000—I'm doubtful. In the 2020s it's ridiculous to say that instruction caching matters.
+A more specific claim about K is that the key to its speed is that the interpreter, or some part of it, fits in L1 cache. I know Arthur Whitney himself has said this; I can't find that now but [here](https://kx.com/blog/what-makes-time-series-database-kdb-so-fast/)'s some material from KX about the "L1/2 cache". Maybe this was a relevant factor in the early days of K around 2000—I'm doubtful. In the 2020s it's ridiculous to say that instruction caching matters.
 
 Let's clarify terms first. The CPU cache is a set of storage areas that are smaller and faster than RAM; memory is copied there when it's used so it will be faster to access it again later. L1 is the smallest and fastest level. On a typical CPU these days it might consist of 64KB of *data* cache for memory to be read and written, and 64KB of *instruction* cache for memory to be executed by the CPU. When I've seen it the L1 cache claim is specifically about the K interpreter (and not the data it works with) fitting in the cache, so it clearly refers to the instruction cache.
 
-A K interpreter will definitely benefit from the instruction cache. Unfortunately, that's where the truth of this claim runs out. Any other interpreter you use will get just about the same benefit, because the most used code will fit in the cache with plenty of room to spare. And the best case you get from a fast core interpreter loop is fast handling of scalar code—exactly the case that array languages typically ignore. K will beat array languages in this area, but lose to interpreters with JIT compilation like JavaScript or CBQN. It might be faster these languages when big arrays are involved, but if so, it has nothing to do with the instruction cache!
+A K interpreter will definitely benefit from the instruction cache. Unfortunately, that's where the truth of this claim runs out. Any other interpreter you use will get just about the same benefit, because the most used code will fit in the cache with plenty of room to spare. And the best case you get from a fast core interpreter loop is fast handling of scalar code—exactly the case that array languages typically ignore.
 
 So, 64KB of instruction cache. That would be small even for a K interpreter. Why is it enough? I claim specifically that while running a program might cause a cache miss once in a while, the total cost of these will only ever be a small fraction of execution time. This is because an interpreter is made of loops: a core loop to run the program as a whole and usually smaller loops for some specific instructions. These loops are small, with the core loop being on the larger side. In fact it can be pretty huge if the interpreter has a lot of exotic instructions, but memory is brought to the cache in lines of around 64 bytes, so that unused regions can be ignored. The active portions might take up a kilobyte or two. Furthermore, you've got the L2 and L3 caches as backup, which are many times larger than L1 and not much slower.
 
@@ -109,4 +111,4 @@ For comparison, here's [ngn/k](https://codeberg.org/ngn/k) (which does aim for a
 
            1.245378356 seconds time elapsed
 
-The stalls are less than 1% here, so maybe the smaller executable is paying off in some way. I can't be sure, because the programs being run are very different: `19.k` is 10 lines while the others are hundreds of lines long. But I don't have a longer K program handy to test with. Again, it doesn't matter much: the point is that the absolute most the other interpreters could gain from being more L1-friendly is about 5% on those fairly representative programs.
+The stalls are less than 1% here, so maybe the smaller executable is paying off in some way. I can't be sure, because the programs being run are very different: `19.k` is 10 lines while the others are hundreds of lines long. But I don't have a longer K program handy to test with (and you could always argue the result doesn't apply to Whitney's K anyway). Again, it doesn't matter much: the point is that the absolute most the other interpreters could gain from being more L1-friendly is about 5% on those fairly representative programs.