CUDA Programming (6) - C/C++/GPU Parallel Computing - Search & Sort

Name: CUDA Programming (6) - C/C++/GPU Parallel Computing - Search & Sort
Price: 49500 KRW
Rating: 5 (7 reviews)

✅ Among the series from (1) to (6), (6) implementation of parallel search and parallel sort ✅ Explains NVIDIA GPU + CUDA programming step-by-step from the basics. ✅ Uses C++/C to process arrays, matrices, image processing, statistical processing, sorting, etc., extremely fast with parallel computing.

(5.0) 7 reviews

170 learners

Level Intermediate

Course period 36 months

onemoresipofcoffee

CUDA

GPU

C++

CUDA

GPU

C++

Reviews from Early Learners

5.0

몽크in도시

5% enrolled

I learned that sort can be surprisingly difficult in CUDA, and yet it is much faster than CPU. Thank you for the great lecture. I feel like I have learned CUDA properly.

5.0

박 신석

100% enrolled

It was great to be able to learn in depth about various techniques and algorithms for sorting!

5.0

8909k

100% enrolled

The lectures are well organized.

What you will gain after the course

Full Series - Massively Parallel Computing with CUDA using GPUs
This lecture is - Part (6) - implementing parallel search and parallel sort
✅Bundle Discount Coupon✳️ provided for the "CUDA Programming" roadmap

Speed is the lifeblood of a program!
Make it fast with massively parallel processing techniques 🚀

I heard massive parallel computing is important 🧐

GPU/graphics card-based massively parallel computing is being very actively used in fields such as AI, deep learning, big data processing, and image/video/audio processing. Currently, the most widely applied technology in GPU parallel computing is NVIDIA's CUDA architecture.

While technologies like massively parallel computing and CUDA are considered crucial within the field of parallel computing, it is often difficult to even begin learning because systematic courses are hard to find. Through this course, you can learn CUDA programming step-by-step. Although CUDA and parallel computing require a theoretical background and can be challenging, you can certainly master them by following the basics along with the rich examples and background explanations provided in this course. This course is planned as a series, ensuring ample instructional time is provided.

In this course, we will explain how C++/C programmers can combine CUDA libraries and C++/C functions to accelerate problems in various fields using large-scale parallel processing techniques. Through this method, you can accelerate existing C++/C programs or develop new algorithms and programs entirely with parallel computing to achieve breakthrough speeds.

📢 Please check before taking the course!

Please ensure you have a hardware environment where NVIDIA CUDA can operate for the practice sessions. A PC or laptop equipped with an NVIDIA GeForce graphics card is absolutely necessary.
While NVIDIA GeForce graphics cards can be used in some cloud environments, cloud settings change frequently and often involve costs. If you are using a cloud environment, you must secure the method for using the graphics card yourself.
You can find detailed information about the lecture practice environment in the <00. Preparation Before the Lecture> video within the curriculum.

Course Features ✨

#1.
Abundant
examples and explanations

CUDA and massively parallel computing require abundant examples and explanations. This lecture series provides a total of over 24 hours of actual instruction time.

#2.
Practice is essential!

Since it is a computer programming course, we emphasize extensive hands-on practice and provide actual working source code so that you can follow along step-by-step.

#3.
Focus on the
important parts!

During the lecture, redundant explanations for previously covered source code are minimized as much as possible, allowing you to focus your learning on the modified parts or the key points that need emphasis.

Recommended for these people 🙋‍♀️

University students who want to add a portfolio of new technologies before getting a job

Programmers who want to drastically improve existing programs

Major researchers who want to know how various applications have been accelerated

Those who want to learn the theory and practice of parallel processing for AI, deep learning, and matrix computation

A sneak peek at course reviews 🏃

*The reviews below are for an external lecture conducted by the instructor on the same topic.

"I knew nothing about parallel algorithms or parallel computing, but
after taking the course, I gained confidence in parallel computing."

"There were many algorithms that I couldn't solve with existing C++ programs,
but through this lecture, I was able to improve them to enable real-time processing!"

"When I mentioned having experience in parallel computing during an interview after taking this course, the interviewers were very surprised.
They said it's not easy to find CUDA or parallel computing courses at the undergraduate level."

CUDA Programming Mastery Roadmap 🛩️

To maximize focus on each topic, the CUDA programming course was designed as a 7-part series with a total duration of over 24 hours.
Each lecture consists of 6 or more sections, and each section covers an independent topic. (The current lecture, Part 0, consists of 2 sections and provides only the Introduction.)
The slides used in the lecture are provided as PDF files, and the program source code used in the sections where hands-on examples are explained is also provided.

Part 0 (1-hour free lecture)

Introduction to MPC and CUDA - This is the introduction section providing an overall overview of MPC and CUDA.

Part 1 (3 hours 40 minutes)

CUDA kernel concepts - Learn the concept of the CUDA kernel, the starting point of CUDA programming, and see parallel computing in action.

Part 2 (4 hours 15 minutes)

vector addition - Presents operations between vectors in the form of 1D arrays through various examples and actually implements the AXPY routine using CUDA.

Part 3 (4 hours 5 minutes)

memory hierarchy - Learn the memory structure, which is the core of CUDA programming. Implement matrix addition, adjacent difference, etc., as examples.

Part 4 (3 hours 45 minutes)

matrix transpose & multiply - Presents operations between 2D array-style matrices through various examples and implements GEMM routines using CUDA.

Part 5 (3 hours 55 minutes)

atomic operation & reduction - Along with an understanding of CUDA control flow, learn everything from problem definitions to solutions for atomic operations and reduction. Also, implement the GEMV routine using CUDA.

Part 6 (3 hours 45 minutes)Current Lecture

search & sort - Learn examples of effectively implementing search-all problems, even-odd sort, bitonic sort, and counting merge sort using the CUDA architecture.

Mastering CUDA Programming and
Large-Scale Parallel Computing!

Q&A 💬

Q. What are the reviews for the paid courses like?

Since the paid lectures are being released sequentially from (1) to (6), the reviews are scattered and currently set to private. The paid lectures have received the following reviews so far.

It was very helpful because you explained in detail the process of maximizing performance by applying various techniques to a single example.
It was much easier to understand because the memory structures and logic were explained through visualization.
While studying AI vaguely, it's great to be able to add in-depth content about devices.
The software installation was well-explained and source code was provided, making it easy to practice.

Q. Is this a lecture that non-majors can take?

C++ programming experience is required to some extent. At the very least, you should have experience with C programming. Although all examples are written as simply as possible, they are all provided in C++/C code, and the functions provided by malloc, memcpy, etc., are not explained separately.
However, if you have an understanding of computer architecture (registers, cache memory, etc.), operating systems (time-sharing, etc.), and compilers (code generation, code optimization), you will be able to understand the course content more deeply.
This course was originally designed as an advanced study for senior computer science majors at four-year universities.

Q. Is there anything I need to prepare before taking the course? Are there any reference materials regarding the course (required environment, other precautions, etc.)?

You must secure a hardware environment where NVIDIA CUDA works for the practice sessions in advance. A PC/laptop equipped with an NVIDIA GeForce graphics card is absolutely necessary.
While NVIDIA GeForce graphics cards can be used in some cloud environments, cloud settings change frequently and often involve costs; therefore, if you are using a cloud environment, you must resolve the method for using the graphics card on your own.

Q. To what level does the course content cover?

Starting from Part 0 and moving up from Part 1 to Part 6, the course requires deeper theory and a greater level of understanding.
We strongly recommend that you watch the courses in order from Part 0 to Part 6.
The counting merge sort covered at the end of Part 6 is a problem difficult enough that even professional researchers may find it hard to follow immediately. However, offline students who followed the curriculum step-by-step more often reported that they were able to understand it without much trouble, building on their learning from the previous sections.

Q. Is there a reason for setting a course enrollment period?

The reason for setting a course enrollment period is that, due to the nature of the computer science field, there is a high possibility that the content of this lecture will already be outdated by that time.
By then, I will see you again in a new course. 😄

Q. Are there subtitles in the videos?

Yes. Currently, all videos include subtitles.
However, some videos added in the future may not have subtitles.

Information regarding fonts used in the lecture materials ✔️

Only free fonts from Google / Adobe were used in the videos and PDF files.
The Korean font used is "Noto Sans KR", and the English fonts used are Source Sans Pro and Source Serif Pro,
All of them can be downloaded for free from the following links. After downloading, you can unzip the file and install it on your PC/laptop by right-clicking.
At https://fonts.google.com/noto/specimen/Noto+Sans+KR, download as a ZIP file via "download family" and install., tải xuống tệp ZIP bằng nút "download family" rồi cài đặt
At https://fonts.google.com/specimen/Source+Sans+Pro, download as a ZIP file via "download family" and install., tải xuống dưới dạng tệp ZIP bằng cách chọn "download family" rồi cài đặt.
At https://fonts.google.com/specimen/Source+Serif+Pro, download as a ZIP file by clicking "download family" and then install., tải xuống tệp ZIP bằng cách chọn "download family" rồi tiến hành cài đặt.

Recommended for
these people

Who is this course right for?

Those who want to accelerate array/matrix/image processing, statistical processing, sorting, etc., using C++ based parallel computing/parallel processing.
Those who want to accelerate their own developed programs using parallel computing/CUDA.
Those who wish to study NVIDIA CUDA programming/CUDA computing from the basics.
Those who wish to study both the theory and practice of GPU parallel processing/parallel computing in a balanced manner.

Need to know before starting?

C++ or C programming experience
It is even better if you have knowledge of computer architecture, registers, caches, time-sharing, etc.

Hello
This is onemoresipofcoffee

9,776

Learners

318

Reviews

Answers

4.9

Rating

Courses

One more cup of drip coffee for the road

Curriculum

All

39 lectures ∙ (3hr 42min)

Course Materials:

Lecture resources

Section 1. 00. Preparation before the lecture (duplicated with the end of Part 0)

2 lectures ∙ (8min)

Section 2. 34. linear search - linear search

5 lectures ∙ (33min)

3. 34-0 opening - opening
00:58
4. 34-1 search naive - simple way (including full PDF file of section)
10:35
5. 34-2 search find - Searching using the find function (including full source code for the section)
09:32
6. 34-3 search cuda - Searching in CUDA (optional - includes common.cpp)
11:15
7. 34-F closing - Closing
01:03

Section 3. 35. search all - find all locations

6 lectures ∙ (28min)

8. 35-0 opening - opening
00:50
9. 35-1 search all - Find all locations (including entire PDF files)
07:18
10. 35-2 search all find - Find all using the find function (including the entire source code of the section)
04:10
11. 35-3 search all block - find all block method
09:14
12. 35-4 search all stride - stride method
06:22
13. 35-F closing - Closing
00:45

Section 4. 36. binary search - binary search

7 lectures ∙ (35min)

Section 5. 37. even odd sort - Even-odd sort

7 lectures ∙ (43min)

Section 6. 38. bitonic sort - bitonic sort

6 lectures ∙ (36min)

Section 7. 39. counting merge sort - counting merge sort

6 lectures ∙ (36min)

Published: 11/14/2022

Last updated: 07/31/2025

Reviews

All

7 reviews

5.0

7 reviews

wayfarecru0581
Reviews 25
∙
Average Rating 5.0
07/21/2023
5
5% enrolled
I learned that sort can be surprisingly difficult in CUDA, and yet it is much faster than CPU. Thank you for the great lecture. I feel like I have learned CUDA properly.
- onemoresipofcoffee
  Instructor
  07/26/2023
  Hello. 🌞 Thank you for your good review. 🍀 I hope you always have a happy time.
hotstone
Reviews 2
∙
Average Rating 5.0
03/20/2023
5
100% enrolled
It was great to be able to learn in depth about various techniques and algorithms for sorting!
- onemoresipofcoffee
  Instructor
  03/21/2023
  Hello. Thank you for your good evaluation. I will try to come back with more diverse lectures. Thank you.
8909k8961
Reviews 1
∙
Average Rating 5.0
08/24/2023
5
100% enrolled
The lectures are well organized.
- onemoresipofcoffee
  Instructor
  08/25/2023
  Hello. 🌞 Thank you for your good review. 🍀 I hope you always have a happy time.
hrham4324
Reviews 2
∙
Average Rating 5.0
08/13/2025
5
31% enrolled
st0584
Reviews 4
∙
Average Rating 5.0
12/02/2025
5
31% enrolled

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A must-take course for anyone thinking of becoming a Windows programmer! Have you studied C/C+, data structures, and operating system theory? If so, studying system programming is the answer now. Now it's time for practice!

Intermediate

C, vc++, system-programming

Windows System Programming - Basics

nullnull8537

Self-Programming Basic Data Structures/Algorithms (C++)

eazuooz

Easily learn data structures and algorithms, which were difficult to learn with C++, in easy steps. Unlike typical lectures, this course combines data structures and algorithms, allowing you to learn them simultaneously while considering their interrelationships.

Basic

Algorithm, C++, C

Self-Programming Basic Data Structures/Algorithms (C++)

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LLM Finetuning : RunPod and Multi-GPU Practice

danielyouk

Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.

Basic

LLM, RunPod, openAI API

LLM Finetuning : RunPod and Multi-GPU Practice

danielyouk

CUDA Programming (6) - C/C++/GPU Parallel Computing - Search & Sort

5.0

What you will gain after the course

I heard massive parallel computing is important 🧐

Course Features ✨

#1.Abundantexamples and explanations

#2.Practice is essential!

#3.Focus on theimportant parts!

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A sneak peek at course reviews 🏃

CUDA Programming Mastery Roadmap 🛩️

Q&A 💬

Information regarding fonts used in the lecture materials ✔️

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#1.
Abundant
examples and explanations

#2.
Practice is essential!

#3.
Focus on the
important parts!

Recommended for
these people

Hello
This is onemoresipofcoffee