Closed fadedbee closed 1 year ago
On my 2500K i5:
Running benches/encode_benchmark.rs (target/release/deps/encode_benchmark-11ec8998e3d558b6)
Symbol size: 1280 bytes (without pre-built plan)
symbol count = 10, encoded 127 MB in 0.807secs, throughput: 1268.8Mbit/s
symbol count = 100, encoded 127 MB in 0.645secs, throughput: 1586.7Mbit/s
symbol count = 250, encoded 127 MB in 0.659secs, throughput: 1552.3Mbit/s
vs
Finished release [optimized] target(s) in 0.52s
Running `target/release/fountain`
992.984852ms to encode 1250MB (10Gbit)
I've heard that there are optimizations to make RaptorQ ~5x faster. However, they are proprietary and I don't know what they are, so I haven't been able to implement them.
I'm no longer working on fountain codes, but sure when you finish it feel free to post the results here. I'd be interested
Closing for now, but feel free to re-open when you have results.
I was looking at your RaptorQ implementation to evaluate the speed of an original fountain code which I have developed. (It's mathematically original, not based on RS, LT or tornado codes.)
On encoding (with a small number of symbols) I see an order of magnitude better performance.