On Thu, 3 Feb 2022 05:29:51 GMT, kabutz <d...@openjdk.java.net> wrote:
>> BigInteger currently uses three different algorithms for multiply. The >> simple quadratic algorithm, then the slightly better Karatsuba if we exceed >> a bit count and then Toom Cook 3 once we go into the several thousands of >> bits. Since Toom Cook 3 is a recursive algorithm, it is trivial to >> parallelize it. I have demonstrated this several times in conference talks. >> In order to be consistent with other classes such as Arrays and Collection, >> I have added a parallelMultiply() method. Internally we have added a >> parameter to the private multiply method to indicate whether the calculation >> should be done in parallel. >> >> The performance improvements are as should be expected. Fibonacci of 100 >> million (using a single-threaded Dijkstra's sum of squares version) >> completes in 9.2 seconds with the parallelMultiply() vs 25.3 seconds with >> the sequential multiply() method. This is on my 1-8-2 laptop. The final >> multiplications are with very large numbers, which then benefit from the >> parallelization of Toom-Cook 3. Fibonacci 100 million is a 347084 bit number. >> >> We have also parallelized the private square() method. Internally, the >> square() method defaults to be sequential. >> >> Some benchmark results, run on my 1-6-2 server: >> >> >> Benchmark (n) Mode Cnt Score >> Error Units >> BigIntegerParallelMultiply.multiply 1000000 ss 4 51.707 >> ± 11.194 ms/op >> BigIntegerParallelMultiply.multiply 10000000 ss 4 988.302 >> ± 235.977 ms/op >> BigIntegerParallelMultiply.multiply 100000000 ss 4 24662.063 >> ± 1123.329 ms/op >> BigIntegerParallelMultiply.parallelMultiply 1000000 ss 4 49.337 >> ± 26.611 ms/op >> BigIntegerParallelMultiply.parallelMultiply 10000000 ss 4 527.560 >> ± 268.903 ms/op >> BigIntegerParallelMultiply.parallelMultiply 100000000 ss 4 9076.551 >> ± 1899.444 ms/op >> >> >> We can see that for larger calculations (fib 100m), the execution is 2.7x >> faster in parallel. For medium size (fib 10m) it is 1.873x faster. And for >> small (fib 1m) it is roughly the same. Considering that the fibonacci >> algorithm that we used was in itself sequential, and that the last 3 >> calculations would dominate, 2.7x faster should probably be considered quite >> good on a 1-6-2 machine. > > kabutz has updated the pull request incrementally with one additional commit > since the last revision: > > Updated comment to include information about performance src/java.base/share/classes/java/math/BigInteger.java line 1603: > 1601: * parallel multiplication algorithm will use more CPU resources > 1602: * to compute the result faster, with no increase in memory > 1603: * consumption. The implNote should cover a space of possible parallel multiply implementations so it doesn't have to be updated as often as the implementation is tuned or adjusted. So I'd prefer to have a statement like "may use more memory" even if the current implementation doesn't actually use more memory. If there are any "contraindications" on when to use the method, they could be listed here too. ------------- PR: https://git.openjdk.java.net/jdk/pull/6409
