Mind-bending new programming language for GPUs just dropped...

Summary

The 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

00:00

🚀 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.

Keywords

💡Parallel Computing

Parallel computing is the simultaneous use of multiple compute resources to solve a computational problem. It enables tasks to be executed concurrently, reducing the overall processing time. In the video, it's highlighted as a 'superpower' that can significantly speed up problem-solving by distributing the workload across multiple CPUs or GPUs.

💡Bend

Bend is the new programming language introduced in the video, promising automatic parallelism. It abstracts the complexities of parallel programming, allowing developers to write high-level code while the language handles parallel execution. The video emphasizes Bend's capability to run code in parallel without needing knowledge of Cuda blocks, locks, or mutexes.

💡Higher Order Virtual Machine (HVM)

The Higher Order Virtual Machine (HVM) is the runtime environment that Bend uses to execute parallel computations. It's based on interaction combinators, a concept from the 1990s, which allows computations to be structured into a graph for parallel execution. The HVM is not used directly but through the Bend language, which simplifies its interface.

💡Interaction Combinators

Interaction combinators are the fundamental components of the HVM used to represent computations as a graph. They facilitate parallel processing by rewriting computations in a way that allows multiple operations to be executed simultaneously. The video describes how these combinators work within Bend to enable automatic parallelism.

💡Fold

Fold is a key construct in Bend, replacing traditional loops. It allows for the parallel consumption of recursive data types, such as lists or trees. The video contrasts fold with Python's for loops, explaining that fold can process data in parallel, thus enhancing performance.

💡Recursive Data Types

Recursive data types are data structures that can be defined in terms of themselves, such as lists or trees. In Bend, these types are processed using folds, enabling parallel operations on their elements. The video uses the example of counting and adding numbers to illustrate how recursive data types are managed in Bend.

💡CUDA

CUDA is a parallel computing platform and programming model developed by NVIDIA for general computing on GPUs. Bend allows code to run on CUDA cores without modification, significantly speeding up computations. The video demonstrates this by showing how a computation can be reduced from minutes to seconds using CUDA with Bend.

💡Race Conditions

Race conditions are a common issue in parallel computing where the output depends on the sequence or timing of uncontrollable events. They can lead to unpredictable behavior and bugs. The video mentions race conditions as one of the complexities that Bend abstracts away, making parallel programming more accessible.

💡Deadlocks

Deadlocks occur in parallel computing when two or more processes are unable to proceed because each is waiting for the other to release a resource. They are one of the pitfalls Bend aims to eliminate by managing parallelism automatically. The video highlights this as part of Bend's promise to simplify parallel programming.

💡Thread Starvation

Thread starvation happens when certain threads are unable to gain regular access to the resources they need for execution. This is another challenge in parallel computing that Bend addresses by ensuring efficient resource allocation. The video notes that Bend's approach helps avoid such issues, enhancing overall performance.

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