Goto based exceptions
The development version of Nim now offers a new exception handling implementation called "goto based exceptions". It can be enabled via the new command line switch --exceptions:goto. This mode is also activated by --gc:arc for the C target.
This mode implements exceptions in a deterministic way: Raising an exception is implemented by setting an internal threadlocal error flag that is queried after every function call that can raise. Neither C's setjmp mechanism is used nor are C++'s exception handling tables. The error path is intertwined with the success path with the resulting instruction cache benefits and drawbacks. Exception handling is fast and deterministic, there are few reasons to avoid it.
To improve the precision of the compiler's abilities to reason about which call "can raise", make wise usage of the .raises:  annotation.
In http://www.filpizlo.com/papers/baker-ccpe09-accurate.pdf J. Baker, et al argue that in Java exceptions are raised more frequently than in C++ code and so this approach even wins over the typical C++ exception implementation which is based on tables and heavily optimized for the "will not throw" case.
In http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2018/p0709r1.pdf Herb Sutter argues that instead of thread local storage the error indicator itself can be kept in a CPU flag speeding up these conditional branches and producing shorter machine code. Nim could do this if only the C compilers exposed the right set of intrinsics. It is my hope that we'll see patches for the common C compilers so that they expose the x86's carry flag; inline assembler does not work well as it effectively destroys the optimizer's ability to reason about the code.
In other words, even faster ways to implement "goto based" exceptions are known and likely to materialize.
Produced machine code
With --opt:size my GCC version 8.1 produces 2 instructions after a call that can raise:
cmp DWORD PTR [rbx], 0 je .L1
This is a memory fetch followed by jump. An ideal implementation would use the carry flag and a single instruction like jc .L1.
However, to see that the current implementation is quite fast already, let us take a look at this benchmark. This is the good old recursive fibonacci stressing the function call overhead but I also added some string handling so that allocation and destructors are involved producing an implicit try..finally statement (the memory of the produced strings must be deallocated):
import strutils proc fib(m: string): int = let n = parseInt(m) if n < 0: raise newException(ValueError, "out of range") elif n <= 1: 1 else: fib($(n-1)) + fib($(n-2)) import std / [times, monotimes, stats] when defined(cpp): echo "C++" elif compileOption("exceptions", "setjmp"): echo "setjmp" elif compileOption("exceptions", "goto"): echo "goto" var r: RunningStat for iterations in 1..5: let start = getMonoTime() for i in 0 ..< 1000: discard fib("24") # 1 1 2 3 5 8 13 r.push float((getMonoTime() - start).inMilliseconds) echo r
I compiled the program in 3 variants:
- nim cpp --gc:arc -d:danger fib.nim
- nim c --gc:arc --exceptions:goto -d:danger fib.nim
- nim c --gc:arc --exceptions:setjmp -d:danger fib.nim
On my machine I got these results:
C++ RunningStat( number of probes: 5 max: 6696.0 min: **6416.0** sum: 32605.0 mean: 6521.0 std deviation: 102.7151400719486 ) goto RunningStat( number of probes: 5 max: 6550.0 min: **6448.0** sum: 32463.0 mean: 6492.6 std deviation: 36.34611396009203 ) setjmp RunningStat( number of probes: 5 max: 8484.0 min: **8331.0** sum: 41911.0 mean: 8382.200000000001 std deviation: 52.82575129612451 )
Looking only at the minimum we see 6416ms for C++'s exception tables, 6448ms for the goto based exception handling and 8331ms for the old setjmp based exception handling.
So in other words, at least for this particular benchmark the new exception implementation is on par with C++'s table based exception handling while offering the already mentioned advantages.
The new implementation is efficient and portable and already the default when compiling via --gc:arc. What I like most about it is that the error handling path is not slow either, this helps library developers: There is no reason to split the API into tryParseInt and parseInt operations because "exceptions should be rare events", whether they are rare or not can depend on your input data.