Mind-bending new programming language for GPUs just dropped...
Summary
TLDRThe video discusses Bend, a new programming language promising seamless parallel computing. Unlike traditional languages, Bend allows code to run in parallel without the complexity of threads, locks, or GPU-specific coding. It uses interaction combinators and the higher-order virtual machine (hvm) to optimize computations across multiple CPU and GPU cores. The language, implemented in Rust and with Python-like syntax, replaces traditional loops with folds for recursive data types, dramatically improving performance. The video showcases a counting algorithm running significantly faster on multiple threads and a GPU, highlighting Bend's potential to revolutionize parallel computing.
Takeaways
- π A new programming language called 'Bend' promises to make parallel computing accessible to all programmers.
- π Parallel computing can significantly speed up problem-solving by using multiple computers or CPU/GPU cores simultaneously.
- πΌ The challenge with parallel computing is managing complexity and avoiding issues like race conditions and deadlocks, similar to conducting a symphony.
- π€ Bend simplifies parallel programming by abstracting away the need for knowledge about CUDA, locks, mutexes, or regex.
- π Bend allows developers to write high-level, Python-like code, with the system handling the parallel execution automatically.
- π The introduction date of Bend is May 17th, 2024, as mentioned in the script.
- π’ In traditional single-threaded languages like Python, only one operation can be performed at a time, limiting performance.
- π Bend structures computations into a graph of 'interaction combinators' which allows for automatic parallelization.
- π οΈ Bend is built on top of a runtime called the 'higher order virtual machine' (HBVM), which was developed in the 1990s.
- π Bend's syntax is similar to Python, making it easy for developers to adopt, and it's implemented in Rust for performance and reliability.
- π§ Bend replaces traditional loops with 'folds' and introduces a 'bend' keyword for creating recursive data types, streamlining parallel data consumption.
- π The script demonstrates a significant performance improvement when running an algorithm with Bend, reducing execution time from 10 minutes to just 1.5 seconds on a GPU.
Q & A
What is the significance of the new programming language mentioned in the script?
-The new programming language, Bend, is significant because it promises to enable parallelism in computing, which can greatly speed up the execution of code by utilizing multiple CPU or GPU cores.
Why is parallel computing considered a superpower for programmers?
-Parallel computing is considered a superpower because it allows programmers to solve complex problems much faster by distributing the workload across multiple processors, thereby reducing the time taken from weeks to days or even seconds.
What are some of the challenges faced when trying to run code in parallel?
-Challenges in parallel computing include managing race conditions, deadlocks, thread starvation, and conflicts with demons. These issues can make the code more complex and potentially lead to incorrect results if not handled properly.
How does Bend simplify the process of writing parallel algorithms?
-Bend simplifies parallel algorithm writing by abstracting away the complexities of CUDA blocks, locks, mutexes, and regexes. It allows developers to write high-level code similar to Python, and the language handles the parallel execution automatically.
What is the main difference between running code in Python and Bend in terms of threading?
-Python code typically runs on a single thread, which means only one instruction can be executed at a time. In contrast, Bend is designed to run computations in parallel by default, utilizing multiple threads without requiring the programmer to manage them explicitly.
How does the concept of interaction combinators in Bend contribute to parallel computation?
-Interaction combinators in Bend structure the elements of computation into a graph, allowing the computation to progress by following a set of rules that rewrite the computation for parallel execution. This continues until all computations are done, and the results are merged back into the function's return expression.
What is the higher order virtual machine (HBVM) and how is it related to Bend?
-The higher order virtual machine (HBVM) is a runtime that implements the concept of interaction combinators dating back to the 1990s. HBVM is not meant for direct use but serves as the underlying technology for Bend, which is a high-level language built to interface with it.
What programming language is Bend implemented in?
-Bend is implemented in Rust, which is known for its performance and safety features.
How does the syntax of Bend compare to Python?
-The syntax of Bend is very similar to Python, making it easier for developers familiar with Python to learn and use Bend.
What is the alternative to loops in Bend, and how does it work?
-In Bend, instead of loops, there is a concept called a 'fold' that works like a search and replace for data types. Folds allow for the consumption of recursive data types in parallel, such as lists or trees.
How does performance differ when running an algorithm on a single thread versus using Bend with multiple threads or on a GPU?
-Running an algorithm on a single thread can take a very long time, such as 10 minutes or more. However, using Bend to run the same code with multiple threads on a CPU can reduce the time to about 30 seconds. Further improvement can be achieved by running the code on a GPU with Bend, which can bring the execution time down to approximately 1.5 seconds.
What is the main takeaway from the script regarding the potential of Bend?
-The main takeaway is that Bend offers a powerful and simplified approach to parallel computing, allowing developers to achieve significant performance improvements without having to deal with the complexities of traditional parallel programming techniques.
Outlines
π Introduction to Bend: The New Parallel Programming Language
The script introduces a new programming language called Bend, which promises to revolutionize parallel computing. It explains that parallel computing can drastically reduce the time required to solve complex problems by utilizing multiple processors simultaneously. However, it also acknowledges the difficulty in managing parallel code, which can lead to issues such as race conditions and deadlocks. Bend offers a solution by simplifying the process, allowing developers to write high-level code that automatically runs in parallel without the need for understanding lower-level concurrency mechanisms. The language is presented as a game-changer, especially for those with multi-core CPUs or GPUs, by making it easy to harness their full potential.
π Understanding Bend's High-Level Parallelism
This paragraph delves deeper into how Bend enables high-level parallelism. It contrasts traditional single-threaded programming with Bend's approach, which structures computations into a graph of interaction combinators. This allows for computations to be automatically rewritten and executed in parallel. The script also introduces the concept of the higher order virtual machine (HVM), which is the underlying runtime that Bend is built upon. Bend itself is implemented in Rust and has a syntax similar to Python, making it accessible to developers familiar with that language. The paragraph also touches on the execution process, explaining how to run Bend code using the 'bend run' command and the difference between sequential and parallel execution.
π Bend's Unique Features: Folds and Unfolds
The script highlights two unique features of Bend: folds and unfolds. Unlike traditional loops found in languages like Python, Bend uses folds to process data types like lists or trees in parallel. Folds act as a search and replace mechanism for data types, allowing for algorithms that would typically require loops to be executed in a parallel fashion. Unfolds are introduced as the counterpart to folds, used to construct recursive data types. The paragraph provides a practical example of how to count and add numbers using a fold in Bend, showcasing the language's ability to handle complex tasks with simplicity and efficiency.
ποΈ Performance Benchmarks: Bend's Speed Advantage
The final paragraph focuses on the performance benefits of using Bend for parallel computing. The script presents a scenario where an algorithm that takes a significant amount of time to run on a single thread can be executed much faster using Bend's parallel capabilities. It demonstrates a dramatic reduction in computation time when the same code is run on multiple CPU threads and then further improved by utilizing the GPU with Cuda. The script concludes with a powerful statement about the potential of Bend to transform programming by making parallel computing accessible and efficient.
Mindmap
Keywords
π‘Parallelism
π‘Bend
π‘High-level language
π‘CPU cores
π‘GPU cores
π‘Instruction per cycle
π‘Race conditions
π‘Fold
π‘Recursive data types
π‘Interaction combinators
π‘Higher Order Virtual Machine (HBVM)
Highlights
A new programming language called Bend promises to simplify parallel computing.
Parallel computing allows solving problems faster by using multiple processors.
Traditional parallel programming is complex and error-prone.
Bend claims to enable parallel execution without knowledge of CUDA, locks, or mutexes.
Bend allows utilizing all CPU cores with high-level, Python-like code.
Python's single-threaded nature limits its performance.
Modern CPUs can perform billions of instructions per second.
Using multiple threads in Python adds complexity and potential issues.
Bend structures computations into a graph with interaction combinators.
Interaction combinators enable automatic parallel execution of computations.
Bend is built on top of the higher order virtual machine (HBVM).
Bend's language is implemented in Rust and has syntax similar to Python.
Bend replaces traditional loops with a concept called 'fold' for parallel data processing.
Bend's 'fold' allows parallel consumption of recursive data types like lists or trees.
Bend's performance is significantly improved by utilizing CPU and GPU cores.
Bend's execution time is drastically reduced when using 'bend run-cu' on a GPU.
Bend demonstrates the potential for easy and efficient parallel programming.
Transcripts
yesterday the clouds opened up and a
weird new programming language came down
to earth with a promise of parallelism
for allou who writeth code this is big
if true because parallel Computing is a
superpower it allows a programmer to
take a problem that could be solved in a
week and instead solve it in seven days
using seven different computers
unfortunately running code in parallel
is like conducting a symphony one wrong
note and the entire thing becomes a
total disaster but luckily Bend offers
Hope by making a bold promise everything
that can run in parallel will run in
parallel you don't need to know anything
about Cuda blocks locks mutexes or
regex's to write algorithms that take
advantage of all 24 of your CPU cores or
even all 16,000 of your GPU cores you
just write some highlevel python looking
code and the rest is Magic it is May
17th 2024 and you're watching the code
report when you write code in a language
like python your code runs on a single
thread that means only one thing can
happen at a time it's like going to a
KFC with only one employee who takes the
order cleans the toilets and Cooks the
food in that order now on a modern CPU
you might have a clock cycle around 4
GHz and if it's handling one instruction
per cycle you're only able to perform 4
billion instructions per second now if
four giips is not enough you can modify
your python code to take advantage of
multiple threads but it adds a lot of
complexity to your code and there's all
kinds of gotas like race conditions
Deadlocks thread starvation and may even
lead to conflicts with demons even if
you do manage to get it working you
might find that your CPU just doesn't
have enough juice at which point you
look into using the thousands of cacor
on your GPU you but now you'll need to
write some C++ code and likely blow your
leg off in the process well what if
there is a language that just knew how
to run things in parallel by default
that's the promise of Bend imagine we
have a computation that adds two
completely random numbers together in
Python The Interpreter is going to
convert this into B code and then
eventually run it on the python virtual
machine pretty simple but in Bend things
are a little more complex the elements
of the computation are structured into a
graph which are called interaction
combinators you can think of it as a big
network of all the computations that
need to be done when two nodes run into
each other the computation progresses by
following a simple set of rules that
rewrite the computation in a way that
can be done in parallel it continues
this pattern until all computations are
done it then merges the result back into
whatever expression was returned from
the function this concept of interaction
combinators goes all the way back to the
1990s and is implemented in a runtime
called the higher order virtual machine
hbm is not meant to be used directly and
that's why they build bend a highle
language to interface with it and the
language itself is implemented in Rust
its syntax is very similar to Python and
we can write a Hello World by defining a
main function that returns a string now
to execute this code we can pull up the
terminal and use the Ben run command by
default this is going to use the rust
interpreter which will execute it
sequentially just like any other boring
language but now here's where things get
interesting imagine we have an algorithm
that needs to count a bunch of numbers
and then add them together the first
thing that might blow your mind is that
bend does not have loops like we can't
just do a for Loop like we would in
Python instead Bend has something
entirely different called a fold that
works like a search and replace for data
types and any algorithm that requires a
loop can be replaced with a fold
basically a fold allows you to consume
recursive data types in parallel like a
list or a tree but first we need to
construct a recursive data type and for
that we have the bend keyword which is
like the opposite of fold now if that's
a little too mind-bending maybe check
out my back catalog for recursion in 100
seconds but now let's see what this
looks like from a performance standpoint
when I try to run this algorithm on a
single thread it takes forever like 10
minutes or more however I can run the
same code without any modification
whatsoever with the bend run C command
when I do that it's now utilizing all 24
threads on my CPU and now it only takes
about 30 seconds to run the computation
that's a huge Improvement but I think we
can still do better because I'm a baller
I have an Nvidia RTX 490 and once again
I can run this code without any
modification on Cuda with Bend run- cuu
and now this code only takes 1 and 1
half seconds to run and I'll just go
ahead and drop the mic right there this
has been the code report thanks for
watching and I will see you in the next
one
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