4 ms·

Just curious: Why is that?

by vonnik 11y ago

elcritch 11y ago
Having done both Java and scientific programming I'd mostly say that the verbosity of Java really distracts away from the task of writing scientific code. An academic really doesn't usually care to learn about singletons and classes to write really fast for-loops. Also while the speed of Java now is awesome, floating point performance is key for those scientists who do know more systems level programming (memory management is a big issues). Actually GC in Julia can be a pain also.
- agibsonccc 11y ago
  Which is why a c++ underbelly is so important. The computations shouldn't happen at the java level if a scientific lib is being done right. I'd say scala isn't a bad alternative to that (which we have nd4s which is a wrapper over nd4j)
  - tanlermin 11y ago
    Ugh. But in julia, its written in julia. There are even julia native tensor and matrix ops.
- noobermin 11y ago
  I have a passing knowledge of Julia (I've written a simple Lorentz Force[0] stepper in it) is there a way to avoid GC in the middle of the calculation, ie., a performance critical loop or other? [0] https://github.com/noobermin/jelo https://github.com/noobermin/jelo
  - ffriend 11y ago
    You can turn garbage collection on and off using `gc_enable` function.
tavert 11y ago
Without easy access to SIMD or value types (and JNI does not count as easy), the JVM's performance for regular computations on large datasets is still pretty lacking. Quoting http://cr.openjdk.java.net/~jrose/values/values-0.html http://cr.openjdk.java.net/~jrose/values/values-0.html, "Numeric types like complex numbers, extended-precision or unsigned integers, and decimal types are widely useful but can only be approximated (to the detriment of type safety and/or performance) by primitives or object classes." And the JVM's costs in terms of deployment and startup times aren't very attractive for scientific workloads. You wouldn't gain as much in a migration from Python or Matlab to Java (or any other JVM language) as you would to C++ (or increasingly Julia), since you'll likely still need access to the same native libraries and JNI is kind of a pain.

Just curious: Why is that?

by vonnik 11y ago

Just curious: Why is that?

Having done both Java and scientific programming I'd mostly say that the verbosity of Java really distracts away from the task of writing scientific code. An academic really doesn't usually care to learn about singletons and classes to write really fast for-loops. Also while the speed of Java now is awesome, floating point performance is key for those scientists who do know more systems level programming (memory management is a big issues). Actually GC in Julia can be a pain also.

agibsonccc 11y ago
Which is why a c++ underbelly is so important. The computations shouldn't happen at the java level if a scientific lib is being done right. I'd say scala isn't a bad alternative to that (which we have nd4s which is a wrapper over nd4j)
- tanlermin 11y ago
  Ugh. But in julia, its written in julia. There are even julia native tensor and matrix ops.
noobermin 11y ago
I have a passing knowledge of Julia (I've written a simple Lorentz Force[0] stepper in it) is there a way to avoid GC in the middle of the calculation, ie., a performance critical loop or other? [0] https://github.com/noobermin/jelo https://github.com/noobermin/jelo
- ffriend 11y ago
  You can turn garbage collection on and off using `gc_enable` function.

tavert 11y ago

Without easy access to SIMD or value types (and JNI does not count as easy), the JVM's performance for regular computations on large datasets is still pretty lacking. Quoting http://cr.openjdk.java.net/~jrose/values/values-0.html http://cr.openjdk.java.net/~jrose/values/values-0.html, "Numeric types like complex numbers, extended-precision or unsigned integers, and decimal types are widely useful but can only be approximated (to the detriment of type safety and/or performance) by primitives or object classes." And the JVM's costs in terms of deployment and startup times aren't very attractive for scientific workloads. You wouldn't gain as much in a migration from Python or Matlab to Java (or any other JVM language) as you would to C++ (or increasingly Julia), since you'll likely still need access to the same native libraries and JNI is kind of a pain.