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

Fireship
17 May 202404:01

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

  • 🌟 A new programming language called 'Bend' promises to make parallel computing accessible to all programmers.
  • πŸ”„ Parallel computing can significantly speed up problem-solving by using multiple computers or CPU/GPU cores simultaneously.
  • 🎼 The challenge with parallel computing is managing complexity and avoiding issues like race conditions and deadlocks, similar to conducting a symphony.
  • πŸ€– Bend simplifies parallel programming by abstracting away the need for knowledge about CUDA, locks, mutexes, or regex.
  • πŸ”‘ Bend allows developers to write high-level, Python-like code, with the system handling the parallel execution automatically.
  • πŸ“… The introduction date of Bend is May 17th, 2024, as mentioned in the script.
  • πŸ”’ In traditional single-threaded languages like Python, only one operation can be performed at a time, limiting performance.
  • πŸ”„ Bend structures computations into a graph of 'interaction combinators' which allows for automatic parallelization.
  • πŸ› οΈ Bend is built on top of a runtime called the 'higher order virtual machine' (HBVM), which was developed in the 1990s.
  • πŸ“ Bend's syntax is similar to Python, making it easy for developers to adopt, and it's implemented in Rust for performance and reliability.
  • πŸ”§ Bend replaces traditional loops with 'folds' and introduces a 'bend' keyword for creating recursive data types, streamlining parallel data consumption.
  • πŸš€ 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

00:00

πŸš€ 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.

πŸ” 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.

πŸ›  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.

🏎️ 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

πŸ’‘Parallelism

Parallelism in computing refers to the simultaneous execution of multiple processes or threads. It is a key concept in the video, as it discusses a new programming language's ability to utilize parallel computing to solve problems more efficiently. The video mentions that parallel computing allows a programmer to use multiple computers to solve a problem faster, which is likened to solving a problem in a week using seven different computers.

πŸ’‘Bend

Bend is the name of the new programming language introduced in the video. It promises to simplify parallel computing by allowing code to run in parallel without the programmer needing to understand complex concepts like CUDA, locks, mutexes, or regex. The language is designed to abstract away the complexities of parallel execution, making it more accessible to developers.

πŸ’‘High-level language

A high-level language is a programming language that is easy for humans to write and understand, as opposed to low-level languages, which are closer to machine code. In the context of the video, Bend is described as a high-level language that looks similar to Python, making it easier for developers to write parallel algorithms without deep knowledge of parallel computing.

πŸ’‘CPU cores

CPU cores are the processing units within a computer's central processing unit (CPU) that perform calculations and execute instructions. The video emphasizes Bend's ability to take advantage of all available CPU cores, such as 24 cores, to run computations in parallel, which significantly speeds up the execution of programs.

πŸ’‘GPU cores

GPU cores are the processing units within a graphics processing unit (GPU) that are designed to handle complex graphical and computational tasks. The video mentions that Bend can utilize up to 16,000 GPU cores for parallel computations, which can drastically reduce the time required to perform intensive calculations.

πŸ’‘Instruction per cycle

Instruction per cycle (IPC) is a measure of how many instructions a CPU can execute in a single clock cycle. The video uses the example of a 4 GHz CPU handling one instruction per cycle, which equates to 4 billion instructions per second, to illustrate the potential computational power that can be harnessed through parallelism.

πŸ’‘Race conditions

A race condition is a situation in programming where the system's behavior is dependent on the sequence or timing of other uncontrollable events. It is mentioned in the video as one of the complexities and 'gotchas' that arise when trying to utilize multiple threads in languages like Python, which can lead to conflicts and bugs.

πŸ’‘Fold

In the context of the video, a fold is a concept in Bend that replaces traditional loops found in languages like Python. It allows for the consumption of recursive data types in parallel, such as lists or trees, making it a powerful tool for parallel computation. The video illustrates this by showing how an algorithm that counts and adds numbers can be implemented using a fold in Bend.

πŸ’‘Recursive data types

Recursive data types are data structures that are defined in terms of themselves. Examples include lists, where each element can be another list, or trees, where each node can have subtrees. The video explains that Bend's fold operation can handle these types of data structures in parallel, which is a significant advantage for certain types of computations.

πŸ’‘Interaction combinators

Interaction combinators are elements of computation structured into a graph, which is used in Bend to represent and manage parallel computations. When two nodes in the graph meet, the computation progresses by following a set of rules that rewrite the computation for parallel execution. This concept is central to how Bend enables automatic parallelism in its programs.

πŸ’‘Higher Order Virtual Machine (HBVM)

The Higher Order Virtual Machine (HBVM) is a runtime environment mentioned in the video that implements the concept of interaction combinators. It is not intended for direct use but serves as the underlying technology that Bend, the high-level language, interfaces with to provide its parallel computing capabilities.

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

00:00

yesterday the clouds opened up and a

00:01

weird new programming language came down

00:03

to earth with a promise of parallelism

00:05

for allou who writeth code this is big

00:08

if true because parallel Computing is a

00:09

superpower it allows a programmer to

00:11

take a problem that could be solved in a

00:13

week and instead solve it in seven days

00:15

using seven different computers

00:16

unfortunately running code in parallel

00:18

is like conducting a symphony one wrong

00:20

note and the entire thing becomes a

00:22

total disaster but luckily Bend offers

00:24

Hope by making a bold promise everything

00:26

that can run in parallel will run in

00:28

parallel you don't need to know anything

00:30

about Cuda blocks locks mutexes or

00:32

regex's to write algorithms that take

00:35

advantage of all 24 of your CPU cores or

00:37

even all 16,000 of your GPU cores you

00:40

just write some highlevel python looking

00:42

code and the rest is Magic it is May

00:44

17th 2024 and you're watching the code

00:47

report when you write code in a language

00:48

like python your code runs on a single

00:50

thread that means only one thing can

00:52

happen at a time it's like going to a

00:54

KFC with only one employee who takes the

00:56

order cleans the toilets and Cooks the

00:57

food in that order now on a modern CPU

01:00

you might have a clock cycle around 4

01:01

GHz and if it's handling one instruction

01:04

per cycle you're only able to perform 4

01:06

billion instructions per second now if

01:08

four giips is not enough you can modify

01:10

your python code to take advantage of

01:12

multiple threads but it adds a lot of

01:14

complexity to your code and there's all

01:16

kinds of gotas like race conditions

01:18

Deadlocks thread starvation and may even

01:20

lead to conflicts with demons even if

01:22

you do manage to get it working you

01:24

might find that your CPU just doesn't

01:25

have enough juice at which point you

01:27

look into using the thousands of cacor

01:29

on your GPU you but now you'll need to

01:31

write some C++ code and likely blow your

01:33

leg off in the process well what if

01:34

there is a language that just knew how

01:36

to run things in parallel by default

01:38

that's the promise of Bend imagine we

01:39

have a computation that adds two

01:41

completely random numbers together in

01:43

Python The Interpreter is going to

01:44

convert this into B code and then

01:46

eventually run it on the python virtual

01:48

machine pretty simple but in Bend things

01:50

are a little more complex the elements

01:52

of the computation are structured into a

01:54

graph which are called interaction

01:55

combinators you can think of it as a big

01:57

network of all the computations that

01:59

need to be done when two nodes run into

02:00

each other the computation progresses by

02:03

following a simple set of rules that

02:04

rewrite the computation in a way that

02:06

can be done in parallel it continues

02:08

this pattern until all computations are

02:09

done it then merges the result back into

02:11

whatever expression was returned from

02:13

the function this concept of interaction

02:15

combinators goes all the way back to the

02:17

1990s and is implemented in a runtime

02:19

called the higher order virtual machine

02:21

hbm is not meant to be used directly and

02:23

that's why they build bend a highle

02:25

language to interface with it and the

02:26

language itself is implemented in Rust

02:29

its syntax is very similar to Python and

02:31

we can write a Hello World by defining a

02:32

main function that returns a string now

02:34

to execute this code we can pull up the

02:36

terminal and use the Ben run command by

02:39

default this is going to use the rust

02:40

interpreter which will execute it

02:42

sequentially just like any other boring

02:44

language but now here's where things get

02:46

interesting imagine we have an algorithm

02:48

that needs to count a bunch of numbers

02:49

and then add them together the first

02:51

thing that might blow your mind is that

02:52

bend does not have loops like we can't

02:54

just do a for Loop like we would in

02:56

Python instead Bend has something

02:58

entirely different called a fold that

03:00

works like a search and replace for data

03:01

types and any algorithm that requires a

03:04

loop can be replaced with a fold

03:05

basically a fold allows you to consume

03:07

recursive data types in parallel like a

03:09

list or a tree but first we need to

03:11

construct a recursive data type and for

03:13

that we have the bend keyword which is

03:15

like the opposite of fold now if that's

03:16

a little too mind-bending maybe check

03:18

out my back catalog for recursion in 100

03:20

seconds but now let's see what this

03:22

looks like from a performance standpoint

03:24

when I try to run this algorithm on a

03:25

single thread it takes forever like 10

03:27

minutes or more however I can run the

03:29

same code without any modification

03:31

whatsoever with the bend run C command

03:33

when I do that it's now utilizing all 24

03:36

threads on my CPU and now it only takes

03:38

about 30 seconds to run the computation

03:40

that's a huge Improvement but I think we

03:42

can still do better because I'm a baller

03:44

I have an Nvidia RTX 490 and once again

03:47

I can run this code without any

03:48

modification on Cuda with Bend run- cuu

03:51

and now this code only takes 1 and 1

03:53

half seconds to run and I'll just go

03:54

ahead and drop the mic right there this

03:56

has been the code report thanks for

03:58

watching and I will see you in the next

03:59

one

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