Java is very strict about floating point arithmetic. There’s even a keyword, strictfp, which allows you to make it stricter, ensuring you’ll get a potentially less precise but identical result wherever you run your program. There’s actually a JEP to make this the only behaviour. JLS 15.18.2 states clearly that floating point addition is not
Continue reading Floating Point: Manual Unrolling or Autovectorisation?
Category: Benchmarking
Faster Floating Point Reductions
At the moment, I am working on a hobby project called SplitMap, which aims to evaluate aggregations over complex boolean expressions as fast as possible using the same high level constructs of the streams API. It’s already capable of performing logic that takes vanilla parallel streams 20ms in under 300μs, but I think sub 100μs
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Matrix Multiplication Revisited
In a recent post, I took a look at matrix multiplication in pure Java, to see if it can go faster than reported in SIMD Intrinsics on Managed Language Runtimes. I found faster implementations than the paper’s benchmarks implied was possible. Nevertheless, I found that there were some limitations in Hotspot’s autovectoriser that I didn’t
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Sum of Squares
Streams and lambdas, especially the limited support offered for primitive types, are a fantastic addition to the Java language. They’re not supposed to be fast, but how do these features compare to a good old for loop? For a simple calculation amenable to instruction level parallelism, I compare modern and traditional implementations and observe the
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The Much Aligned Garbage Collector
A power of two is often a good choice for the size of an array. Sometimes you might see this being exploited to replace an integer division with a bitwise intersection. You can see why with a toy benchmark of a bloom filter, which deliberately folds in a representative cost of a hash function and
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