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Bradlee Speice
2020-02-23 18:14:16 -05:00
committed by Bradlee Speice
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_posts/2019-06-31-high-performance-systems.md
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@ -48,7 +48,7 @@ Even though everyone has different needs, there are still common things to look
- [In Python](https://rushter.com/blog/python-garbage-collector/), individual objects are collected if the reference count reaches 0, and each generation is collected if `num_alloc - num_dealloc > gc_threshold` whenever an allocation happens. The GIL is acquired for the duration of generational collection.
- Java has [many](https://docs.oracle.com/en/java/javase/12/gctuning/parallel-collector1.html#GUID-DCDD6E46-0406-41D1-AB49-FB96A50EB9CE) [different](https://docs.oracle.com/en/java/javase/12/gctuning/garbage-first-garbage-collector.html#GUID-ED3AB6D3-FD9B-4447-9EDF-983ED2F7A573) [collection](https://docs.oracle.com/en/java/javase/12/gctuning/garbage-first-garbage-collector-tuning.html#GUID-90E30ACA-8040-432E-B3A0-1E0440AB556A) [algorithms](https://docs.oracle.com/en/java/javase/12/gctuning/z-garbage-collector1.html#GUID-A5A42691-095E-47BA-B6DC-FB4E5FAA43D0) to choose from, each with different characteristics. The default algorithms (Parallel GC in Java 8, G1 in Java 9) freeze the JVM while collecting, while more recent algorithms ([ZGC](https://wiki.openjdk.java.net/display/zgc) and [Shenandoah](https://wiki.openjdk.java.net/display/shenandoah)) are designed to keep "stop the world" to a minimum by doing collection work in parallel.
**Allocation**: Every language has a different way of interacting with "heap" memory, but the principle is the same: running the allocator to allocate/deallocate memory takes time that can often be put to better use. Understanding when your language interacts with the allocator is crucial, and not always obvious. For example: C++ and Rust don't allocate heap memory for iterators, but Java does (meaning potential GC pauses). Take time to understand heap behavior (I made a [a guide for Rust](https://speice.io/2019/02/understanding-allocations-in-rust.html)), and look into alternative allocators ([jemalloc](http://jemalloc.net/), [tcmalloc](https://gperftools.github.io/gperftools/tcmalloc.html)) that might run faster than the operating system default.
**Allocation**: Every language has a different way of interacting with "heap" memory, but the principle is the same: running the allocator to allocate/deallocate memory takes time that can often be put to better use. Understanding when your language interacts with the allocator is crucial, and not always obvious. For example: C++ and Rust don't allocate heap memory for iterators, but Java does (meaning potential GC pauses). Take time to understand heap behavior (I made a [a guide for Rust](/2019/02/understanding-allocations-in-rust.html)), and look into alternative allocators ([jemalloc](http://jemalloc.net/), [tcmalloc](https://gperftools.github.io/gperftools/tcmalloc.html)) that might run faster than the operating system default.
**Data Layout**: How your data is arranged in memory matters; [data-oriented design](https://www.youtube.com/watch?v=yy8jQgmhbAU) and [cache locality](https://www.youtube.com/watch?v=2EWejmkKlxs&feature=youtu.be&t=1185) can have huge impacts on performance. The C family of languages (C, value types in C#, C++) and Rust all have guarantees about the shape every object takes in memory that others (e.g. Java and Python) can't make. [Cachegrind](http://valgrind.org/docs/manual/cg-manual.html) and kernel [perf](https://perf.wiki.kernel.org/index.php/Main_Page) counters are both great for understanding how performance relates to memory layout.

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_posts/2019-12-14-release-the-gil.md
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@ -6,9 +6,9 @@ category:
tags: [python]
---
Complaining about the [Global Interpreter Lock](https://wiki.python.org/moin/GlobalInterpreterLock)(GIL) seems like a rite of passage for Python developers. It's easy to criticize a design decision made before multi-core CPU's were widely available, but the fact that it's still around indicates that it generally works [Good](https://wiki.c2.com/?PrematureOptimization) [Enough](https://wiki.c2.com/?YouArentGonnaNeedIt). Besides, there are simple and effective workarounds; it's not hard to start a [new process](https://docs.python.org/3/library/multiprocessing.html) and use message passing to synchronize code running in parallel.
Complaining about the [Global Interpreter Lock](https://wiki.python.org/moin/GlobalInterpreterLock) (GIL) seems like a rite of passage for Python developers. It's easy to criticize a design decision made before multi-core CPU's were widely available, but the fact that it's still around indicates that it generally works [Good](https://wiki.c2.com/?PrematureOptimization) [Enough](https://wiki.c2.com/?YouArentGonnaNeedIt). Besides, there are simple and effective workarounds; it's not hard to start a [new process](https://docs.python.org/3/library/multiprocessing.html) and use message passing to synchronize code running in parallel.
Still, wouldn't it be nice to have more than a single active interpreter thread? In an age of asynchronicity and $M:N$ threading, Python seems lacking. The ideal scenario is to take advantage of both Python's productivity and the modern CPU's parallel capabilities.
Still, wouldn't it be nice to have more than a single active interpreter thread? In an age of asynchronicity and *M:N* threading, Python seems lacking. The ideal scenario is to take advantage of both Python's productivity and the modern CPU's parallel capabilities.
Presented below are two strategies for releasing the GIL's icy grip without giving up on what makes Python a nice language to start with. Bear in mind: these are just the tools, no claim is made about whether it's a good idea to use them. Very often, unlocking the GIL is an [XY problem](https://en.wikipedia.org/wiki/XY_problem); you want application performance, and the GIL seems like an obvious bottleneck. Remember that any gains from running code in parallel come at the expense of project complexity; messing with the GIL is ultimately messing with Python's memory model.
@ -71,7 +71,6 @@ _ = cython_gil(N);
> <pre>
> CPU times: user 365 ms, sys: 0 ns, total: 365 ms
>
> Wall time: 372 ms
> </pre>