Discover and read the best of Twitter Threads about #projectloom
Most recents (4)
1. The unclear licensing model has been the most significant barrier to GraalVM adoption for years. However, at @JavaOne 2022, it was announced that @Oracle will make the GraalVM CE a part of the OpenJDK. Project Galahad has been set up to carry out the process - ❤️ that name. 
2. @Google announced that it was joining the @adoptium working group as a Strategic Member. Google didn't just limit itself to throwing piles of 💰 at JDK, but together with @AlibabaGroup began working on Java Class Pre-Initialization to accelerate the cold start of applications.
#Java18 comes out later today - what can you expect from Java's newest release? Here's a 🧵 with one tweet per feature - short and sweet.
For more details and links, read this blog post: nipafx.dev/inside-java-ne…
For questions, join our #JDK18 AMA later today. 👇🏾
For more details and links, read this blog post: nipafx.dev/inside-java-ne…
For questions, join our #JDK18 AMA later today. 👇🏾
Pattern Matching for `switch` (2nd preview)
openjdk.java.net/jeps/420
Changes:
* constant case labels must now appear before guarded patterns of the same type
* exhaustiveness checking is now more precise when sealed classes and generics mix
Another preview is expected in #JDK19.
openjdk.java.net/jeps/420
Changes:
* constant case labels must now appear before guarded patterns of the same type
* exhaustiveness checking is now more precise when sealed classes and generics mix
Another preview is expected in #JDK19.
Vector API (3rd incubation)
openjdk.java.net/jeps/417
Changes:
* support for ARM Scalar Vector Extension (SVE)
* performance for masked ops improved on architectures that support masking in hardware
Vector API will stay incubating until Valhalla's value/primitive types.
openjdk.java.net/jeps/417
Changes:
* support for ARM Scalar Vector Extension (SVE)
* performance for masked ops improved on architectures that support masking in hardware
Vector API will stay incubating until Valhalla's value/primitive types.
#ProjectLoom's virtual threads will make high performance in concurrent systems attainable with much simpler code. But Loom aims for even more and wants to make the code clearer and more robust by introducing *structured concurrency*.
Here's what that's all about. 🧵
Here's what that's all about. 🧵
Important: This is about concurrency, not parallelism. See yesterday's thread 👇🏾 for a more detailed distinction, but the gist is that concurrency is about processing lots of tasks that the environment throws at your system at the same time, ideally with high throughput.
Concurrency code is often unstructured:
* splitting and joining concurrent subtasks in different methods/classes
* little support to compose error-handling, cancellation, … across subtasks
* threading structure is opaque to the runtime, which only sees independent threads
* …
* splitting and joining concurrent subtasks in different methods/classes
* little support to compose error-handling, cancellation, … across subtasks
* threading structure is opaque to the runtime, which only sees independent threads
* …
What's it called when multiple threads execute at the same time? Parallelism? Concurrency? 🤔 Is there a difference? (Spoiler: Yes!)
Let me explain in a few tweets, ripping off @pressron's #InsideJava blog post "On Parallelism and Concurrency". 🧵
inside.java/2021/11/30/on-…
Let me explain in a few tweets, ripping off @pressron's #InsideJava blog post "On Parallelism and Concurrency". 🧵
inside.java/2021/11/30/on-…
Parallelism:
Taking a task and splitting it up, so multiple CPUs can compute partial solutions in parallel to solve the task in less wall-clock time.
Concurrency:
Having a number of tasks that need to be arranged in a way that solves as many of them per $time_unit as possible.
Taking a task and splitting it up, so multiple CPUs can compute partial solutions in parallel to solve the task in less wall-clock time.
Concurrency:
Having a number of tasks that need to be arranged in a way that solves as many of them per $time_unit as possible.
Some parallelizable tasks:
* sorting an array
* inverting a matrix
* rendering graphics
Some examples for concurrency:
* processing incoming web requests
* making outbound calls to DB and/or other web services
* observe file system for changes
* sorting an array
* inverting a matrix
* rendering graphics
Some examples for concurrency:
* processing incoming web requests
* making outbound calls to DB and/or other web services
* observe file system for changes
