array
ghost-chase-condition
Our great sponsors
| array | ghost-chase-condition | |
|---|---|---|
| 4 | 5 | |
| 188 | 2 | |
| - | - | |
| 6.9 | 4.7 | |
| 4 months ago | about 2 years ago | |
| C++ | Java | |
| Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
array
-
Benchmarking 20 programming languages on N-queens and matrix multiplication
I should have mentioned somewhere, I disabled threading for OpenBLAS, so it is comparing one thread to one thread. Parallelism would be easy to add, but I tend to want the thread parallelism outside code like this anyways.
As for the inner loop not being well optimized... the disassembly looks like the same basic thing as OpenBLAS. There's disassembly in the comments of that file to show what code it generates, I'd love to know what you think is lacking! The only difference between the one I linked and this is prefetching and outer loop ordering: https://github.com/dsharlet/array/blob/master/examples/linea...
-
A basic introduction to NumPy's einsum
If you are looking for something like this in C++, here's my attempt at implementing it: https://github.com/dsharlet/array#einstein-reductions
It doesn't do any automatic optimization of the loops like some of the projects linked in this thread, but, it provides all the tools needed for humans to express the code in a way that a good compiler can turn it into really good code.
ghost-chase-condition
-
Why would a Java prime sieve run at only half its speed _some_ of the times?
Thanks! It does and it does not. Here's 100 rounds where it does: https://github.com/PEZ/ghost-chase-condition/tree/master/tes...
But in other cases I have it seems to not help that much. Not sure how to minimize those, but will try figure it out.
What are some alternatives?
optimizing-the-memory-layout-of-std-tuple - Optimizing the memory layout of std::tuple
Paguro - Generic, Null-safe, Immutable Collections and Functional Transformations for the JVM
NumPy - The fundamental package for scientific computing with Python.
.NET Runtime - .NET is a cross-platform runtime for cloud, mobile, desktop, and IoT apps.
cadabra2 - A field-theory motivated approach to computer algebra.
for-linux - Docker Engine for Linux
alphafold2 - To eventually become an unofficial Pytorch implementation / replication of Alphafold2, as details of the architecture get released
java-perf-workshop - Guided walkthrough to understand the performance aspects of a Java web service
einops - Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
async-profiler - Sampling CPU and HEAP profiler for Java featuring AsyncGetCallTrace + perf_events [Moved to: https://github.com/async-profiler/async-profiler]
Einsum.jl - Einstein summation notation in Julia
gctoolkit - Tool for parsing GC logs