Mind-bending new programming language for GPUs just dropped...
Summary
TLDRThe Code Report introduces Bend, a revolutionary programming language that simplifies parallel computing. Traditionally complex and error-prone, parallelism is now accessible through Bend's high-level syntax resembling Python. By leveraging interaction combinators and a graph-based computation model, Bend automatically optimizes code to run on multiple CPU and GPU cores. This results in significant performance improvements, as demonstrated by an algorithm that runs in minutes on a single thread but seconds on multiple threads and even faster on an Nvidia RTX GPU, showcasing Bend's potential to transform coding efficiency.
Takeaways
- 🌟 A new programming language called Bend has emerged, promising to simplify parallel computing for developers.
- 🔄 Parallel computing is likened to a superpower, allowing complex problems to be solved much faster using multiple processors.
- 🎼 The challenge of parallel computing is compared to conducting a symphony, where one wrong move can lead to disaster.
- 🚀 Bend claims to handle parallel execution automatically, requiring no knowledge of CUDA, locks, mutexes, or regex from the programmer.
- 🛠️ Bend's syntax is similar to Python, making it accessible for developers familiar with high-level languages.
- 📚 The concept of interaction combinators, foundational to Bend, dates back to the 1990s and is implemented in a runtime called the Higher Order Virtual Machine (HOVM).
- 💻 Bend is implemented in Rust, ensuring performance and reliability for the language's execution environment.
- 🔧 Bend replaces traditional loops with 'folds', a feature that allows for parallel processing of recursive data types like lists or trees.
- 🔄 The 'bend' keyword in Bend is used to construct recursive data types, which is the counterpart to the 'fold' operation.
- ⏱️ A significant performance boost is demonstrated when running an algorithm on Bend, reducing execution time from minutes to seconds.
- 🎉 Bend's ability to utilize both CPU and GPU resources without code modification showcases its potential for high-performance computing.
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 without requiring the programmer to have knowledge of complex parallel programming techniques such as CUDA, locks, mutexes, or regex.
Why is parallel computing considered a 'superpower' for programmers?
-Parallel computing is considered a 'superpower' because it allows programmers to solve problems much faster by utilizing multiple processors or cores simultaneously, potentially reducing the time from weeks to days.
What is the main challenge with running code in parallel?
-Running code in parallel is challenging because it requires careful management to avoid issues such as race conditions, deadlocks, and thread starvation, which can lead to disastrous results if not handled correctly.
How does Bend simplify the process of writing parallel algorithms?
-Bend simplifies the process by allowing programmers to write high-level code similar to Python, with the language's runtime taking care of the parallel execution details automatically.
What is the difference between running code in a single thread and using multiple threads?
-Running code in a single thread means only one operation can happen at a time, limiting the performance. Using multiple threads allows for concurrent operations, significantly increasing the efficiency and speed of execution.
What is the role of 'interaction combinators' in Bend?
-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.
Why is the Higher Order Virtual Machine (HBVM) not meant to be used directly?
-HBVM is a lower-level runtime that implements the concept of interaction combinators. It is not meant to be used directly because it is more complex and less accessible to programmers, which is why Bend was built as a higher-level language to interface with it.
How is Bend's syntax similar to Python, and what is its implementation language?
-Bend's syntax is designed to be very similar to Python, making it easy for developers familiar with Python to learn and use. The language itself is implemented in Rust.
What is the 'fold' operation in Bend, and how does it differ from loops in other languages?
-In Bend, the 'fold' operation is used instead of loops. It works like a search and replace for data types, allowing recursive data types to be consumed in parallel, which is a different approach from the iterative loops found in languages like Python.
How does the performance of an algorithm change when executed with Bend's parallel capabilities?
-The performance of an algorithm can be significantly improved when executed with Bend's parallel capabilities. The script demonstrates an algorithm that takes 10 minutes or more on a single thread, but only about 30 seconds when utilizing all 24 threads on a CPU, and even faster on a GPU.
What command is used to execute Bend code with GPU acceleration?
-The command 'bend run-cu' is used to execute Bend code with GPU acceleration, allowing the code to run on CUDA-enabled GPUs for further performance improvements.
Outlines
🚀 Introduction to Bend: The Promise of Parallelism
The script introduces a new programming language called Bend, which promises to revolutionize parallel computing. It discusses the challenges of parallelism in programming, where traditional languages require deep knowledge of concurrency mechanisms and can lead to complex issues like race conditions and deadlocks. Bend aims to simplify this by allowing code to run in parallel without the need for understanding these complexities. The script humorously compares the limitations of single-threaded execution to a KFC with only one employee handling all tasks, highlighting the inefficiency of not utilizing multiple cores in modern CPUs and GPUs. It introduces the concept of interaction combinators, a foundational concept in Bend that allows computations to be structured into a graph, facilitating parallel execution.
Mindmap
Keywords
💡Parallel Computing
💡Bend
💡Higher Order Virtual Machine (HVM)
💡Interaction Combinators
💡Fold
💡Recursive Data Types
💡CUDA
💡Race Conditions
💡Deadlocks
💡Thread Starvation
Highlights
Introduction of a new programming language called Bend that promises parallelism for all.
Parallel computing enables solving complex problems faster by using multiple computers simultaneously.
Bend simplifies parallel programming by handling complexities like Cuda, locks, mutexes, or regex without user knowledge.
Bend allows for high-level Python-like code that automatically utilizes all available CPU cores.
Traditional single-threaded programming is compared to a KFC with only one employee, highlighting inefficiency.
Modern CPUs can perform up to 4 billion instructions per second, but this can be improved with parallelism.
Using multiple threads in Python adds complexity and potential issues like race conditions and deadlocks.
Bend offers a language that runs things in parallel by default, simplifying the use of GPU cores.
Bend's computation elements are structured into a graph using interaction combinators for parallel execution.
The concept of interaction combinators dates back to the 1990s and is implemented in the higher order virtual machine.
Bend is a high-level language implemented in Rust, with syntax similar to Python, making it accessible.
Bend replaces traditional loops with 'folds' that allow for parallel consumption of recursive data types.
Bend's 'bend' keyword is used to construct recursive data types, which is the opposite of the 'fold' operation.
Performance comparison shows a significant speedup when using Bend's parallel capabilities on CPU and GPU.
Bend's execution time drastically reduces from 10 minutes on a single thread to 30 seconds on 24 CPU threads.
Further performance improvement is demonstrated by running Bend code on an Nvidia RTX 490 GPU, taking only 1.5 seconds.
The Code Report concludes with a mic drop moment, emphasizing the impressive capabilities of the Bend programming language.
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
5.0 / 5 (0 votes)