Yes. For your goal, I would design Kernel Programming & Internals as a combined low-level course family with two major outcomes:
- AI/ML systems: CPU/GPU kernels, CUDA, memory hierarchy, Tensor Cores, Transformer kernels, inference optimization.
- Cybersecurity: Linux kernel, syscalls, virtual memory, processes, ELF, kernel modules, debugging, vulnerability analysis and defensive security.
The key is to start from C/CPU internals → Linux kernel → memory → GPU/CUDA kernels → AI kernels, rather than jumping directly into CUDA. CUDA kernels execute many threads in parallel and expose thread hierarchy, shared memory, synchronization, and multiple memory spaces, which makes the GPU side naturally connected to the low-level concepts. (NVIDIA Docs)
Kernel Programming & Internals — Complete Course Design
Course architecture
1KERNEL PROGRAMMING & INTERNALS 2│ 3├── COURSE 1 — C/C++ SYSTEMS & MEMORY INTERNALS 4│ 5├── COURSE 2 — CPU + ASSEMBLY INTERNALS 6│ 7├── COURSE 3 — LINUX SYSTEM PROGRAMMING 8│ 9├── COURSE 4 — LINUX KERNEL INTERNALS 10│ 11├── COURSE 5 — MEMORY MANAGEMENT & VIRTUAL MEMORY 12│ 13├── COURSE 6 — PROCESS / ELF / SYSCALL INTERNALS 14│ 15├── COURSE 7 — KERNEL SECURITY & VULNERABILITY ANALYSIS 16│ 17├── COURSE 8 — CUDA GPU KERNEL PROGRAMMING 18│ 19├── COURSE 9 — GPU MEMORY & PERFORMANCE INTERNALS 20│ 21├── COURSE 10 — AI/TRANSFORMER KERNEL PROGRAMMING 22│ 23├── COURSE 11 — AI INFERENCE & GPU SYSTEM OPTIMIZATION 24│ 25└── COURSE 12 — ADVANCED KERNEL ENGINEERING
COURSE 1 — C/C++ Systems & Memory Internals
Goal: Understand what actually happens underneath C/C++ code.
Modules
101. C compilation pipeline 202. Preprocessor 303. Compiler 404. Assembly generation 505. Assembler 606. Linker 707. ELF executable 808. Stack 909. Heap 1010. Global/static memory 1111. Pointers 1212. Function pointers 1313. Struct layout 1414. Alignment 1515. Padding 1616. malloc/free internals 1717. Memory allocator design 1818. Buffer management 1919. Memory corruption 2020. Undefined behavior
Projects
- Build a mini
malloc() - Build a mini memory allocator
- Implement a memory pool
- Stack-frame visualizer
- Heap debugger
- Buffer-overflow laboratory in a controlled environment
AI connection
Understanding:
1tensor memory 2↓ 3pointer 4↓ 5contiguous memory 6↓ 7stride 8↓ 9cache 10↓ 11GPU memory
Cybersecurity connection
1pointer bugs 2↓ 3buffer overflow 4↓ 5use-after-free 6↓ 7memory corruption 8↓ 9vulnerability analysis
COURSE 2 — CPU + Assembly Internals
Modules
101. CPU architecture 202. Registers 303. Instruction Pointer 404. Stack Pointer 505. Flags 606. ALU 707. Control unit 808. CPU cache 909. L1/L2/L3 1010. Branch prediction 1111. Instruction pipeline 1212. Out-of-order execution 1313. SIMD 1414. x86-64 assembly 1515. Function calling convention 1616. System-call instruction 1717. Interrupts 1818. Context switching
Projects
1C program 2 ↓ 3assembly 4 ↓ 5machine instructions 6 ↓ 7CPU execution
Build:
- Assembly calculator
- Function-call tracer
- Context-switch demonstration
- CPU-cache benchmark
COURSE 3 — Linux System Programming
Modules
101. Linux architecture 202. User space 303. Kernel space 404. Processes 505. Threads 606. fork() 707. exec() 808. wait() 909. pipes 1010. signals 1111. shared memory 1212. mmap() 1313. files 1414. file descriptors 1515. sockets 1616. epoll 1717. pthreads 1818. synchronization 1919. mutex 2020. semaphore 2121. atomics
Most important architecture
1Application 2 ↓ 3libc 4 ↓ 5System Call 6 ↓ 7Linux Kernel 8 ↓ 9Hardware
Linux system calls are one of the primary interfaces between userspace and the kernel. (Linux Kernel Archives)
Project
Build a mini shell:
1myshell 2 ├── fork() 3 ├── exec() 4 ├── pipe() 5 ├── dup2() 6 ├── wait() 7 └── signals
COURSE 4 — Linux Kernel Internals
This becomes the core cybersecurity kernel course.
Modules
101. Linux kernel source tree 202. Kernel compilation 303. Kernel configuration 404. Boot process 505. Kernel initialization 606. task_struct 707. scheduler 808. process management 909. kernel threads 1010. interrupts 1111. system calls 1212. VFS 1313. filesystems 1414. device drivers 1515. kernel modules 1616. workqueues 1717. timers 1818. locks 1919. spinlocks 2020. RCU 2121. atomic operations 2222. kernel debugging
Hands-on
Write:
1hello_kernel.c
Then progress to:
1hello module 2 ↓ 3module parameters 4 ↓ 5proc filesystem 6 ↓ 7character device 8 ↓ 9custom syscall
The Linux kernel documentation specifically describes the process and considerations involved in adding a system call. (Linux Kernel Archives)
COURSE 5 — Memory Management & Virtual Memory
This should be one of the deepest courses.
Modules
101. Physical memory 202. Virtual memory 303. Virtual address 404. Physical address 505. MMU 606. Page tables 707. Page directory 808. TLB 909. Page faults 1010. Demand paging 1111. Copy-on-write 1212. mmap 1313. brk 1414. malloc 1515. Kernel allocator 1616. Buddy allocator 1717. Slab/SLUB 1818. Huge pages 1919. NUMA 2020. Memory-mapped files
Core architecture
1CPU 2 │ 3 ▼ 4Virtual Address 5 │ 6 ▼ 7MMU 8 │ 9 ▼ 10TLB 11 │ 12 ▼ 13Page Table 14 │ 15 ▼ 16Physical Address 17 │ 18 ▼ 19RAM
Project
Build a simplified:
1Virtual Memory Simulator
with:
1virtual address 2 ↓ 3page number 4 ↓ 5page table 6 ↓ 7physical frame 8 ↓ 9physical memory
AI connection
This directly prepares you for:
1CPU RAM 2↓ 3PCIe 4↓ 5GPU VRAM 6↓ 7GPU memory hierarchy 8↓ 9tensor allocation 10↓ 11KV cache
COURSE 6 — Process / ELF / Syscall Internals
This course connects everything together.
Main architecture
1/usr/bin/program 2 ↓ 3ELF 4 ↓ 5dynamic linker 6 ↓ 7shared libraries 8 ↓ 9process 10 ↓ 11virtual address space 12 ↓ 13syscalls 14 ↓ 15kernel 16 ↓ 17hardware
Modules
1ELF header 2Program headers 3Sections 4.text 5.data 6.bss 7.rodata 8PLT 9GOT 10dynamic linking 11relocations 12symbols 13shared libraries 14loader 15ASLR 16process memory 17/proc/<pid> 18syscalls 19strace 20ltrace
Project
Build an:
ELF Analyzer
1elf_analyzer program 2 3ELF Header 4Sections 5Segments 6Symbols 7Dynamic Libraries 8Entry Point 9Memory Layout
COURSE 7 — Kernel Security & Vulnerability Analysis
This is the cybersecurity specialization.
Modules
101. Security boundaries 202. User/kernel isolation 303. Privilege levels 404. Linux permissions 505. Capabilities 606. namespaces 707. cgroups 808. seccomp 909. ASLR 1010. DEP/NX 1111. stack canaries 1212. KASLR 1313. SMEP 1414. SMAP 1515. race conditions 1616. TOCTOU 1717. integer overflow 1818. buffer overflow 1919. use-after-free 2020. double-free 2121. kernel attack surface 2222. kernel fuzzing 2323. vulnerability triage 2424. defensive patch analysis
Safe labs
1Vulnerable toy driver 2 ↓ 3Find bug 4 ↓ 5Understand root cause 6 ↓ 7Patch 8 ↓ 9Regression test
The focus should be controlled educational targets and defensive analysis, not exploitation of real systems.
COURSE 8 — CUDA GPU Kernel Programming
Now transition from CPU/kernel programming to GPU kernels.
NVIDIA's CUDA model defines a kernel as a function executed by many CUDA threads, with thread blocks and grids forming the execution hierarchy. (NVIDIA Docs)
Modules
101. GPU architecture 202. CPU vs GPU 303. CUDA architecture 404. Host/device model 505. CUDA kernel 606. threadIdx 707. blockIdx 808. blockDim 909. gridDim 1010. warps 1111. SIMT 1212. synchronization 1313. CUDA streams 1414. events 1515. atomics 1616. error handling 1717. nvcc 1818. CUDA runtime
Projects
1Vector Add 2 ↓ 3Vector Multiply 4 ↓ 5Reduction 6 ↓ 7Histogram 8 ↓ 9Prefix Sum 10 ↓ 11Matrix Multiplication
COURSE 9 — GPU Memory & Performance Internals
This is extremely important for Frontier AI engineering.
CUDA exposes multiple memory spaces, including per-thread local memory, block-level shared memory, and global memory. (NVIDIA Docs)
Modules
101. GPU memory architecture 202. Global memory 303. Shared memory 404. Registers 505. Local memory 606. Constant memory 707. Texture memory 808. L2 cache 909. Memory coalescing 1010. Shared-memory bank conflicts 1111. Occupancy 1212. register pressure 1313. latency 1414. bandwidth 1515. compute-bound kernels 1616. memory-bound kernels 1717. asynchronous execution 1818. CUDA streams 1919. CUDA graphs 2020. profiling
Projects
1Naive GEMM 2 ↓ 3Tiled GEMM 4 ↓ 5Shared-memory GEMM 6 ↓ 7Register-tiled GEMM 8 ↓ 9Tensor Core GEMM
COURSE 10 — AI / Transformer Kernel Programming
This is the AI specialization.
Module 1 — Neural-network kernels
1Vector operations 2Matrix multiplication 3Bias 4ReLU 5GELU 6SiLU 7SwiGLU 8Softmax 9LayerNorm 10RMSNorm
Module 2 — Transformer kernels
1Q projection 2K projection 3V projection 4QKᵀ 5Scaling 6Masking 7Softmax 8Attention × V 9Output projection
Module 3 — Modern Transformer kernels
1RoPE 2GQA 3MQA 4RMSNorm 5SwiGLU 6MoE 7MLA 8KV Cache
Module 4 — Attention optimization
1Naive Attention 2 ↓ 3Tiled Attention 4 ↓ 5Memory-efficient Attention 6 ↓ 7FlashAttention-style kernel
Major project
Build a:
Transformer CUDA Kernel Library
1kernels/ 2├── matmul.cu 3├── softmax.cu 4├── rmsnorm.cu 5├── rope.cu 6├── silu.cu 7├── swiglu.cu 8├── attention.cu 9├── gqa.cu 10├── kv_cache.cu 11└── fused_transformer.cu
COURSE 11 — AI Inference & GPU System Optimization
Modules
101. GPU inference architecture 202. Model loading 303. Weight memory 404. KV cache 505. Prefill 606. Decode 707. Batch processing 808. Continuous batching 909. Memory fragmentation 1010. Quantization 1111. INT8 1212. INT4 1313. FP16 1414. BF16 1515. FP8 1616. Tensor Cores 1717. kernel fusion 1818. CUDA graphs 1919. multi-stream execution 2020. multi-GPU 2121. NCCL 2222. inference profiling
Project
Build a small:
1LLM Inference Engine
architecture:
1Tokenizer 2 ↓ 3Embedding 4 ↓ 5Transformer 6 ↓ 7CUDA kernels 8 ↓ 9KV Cache 10 ↓ 11Sampling 12 ↓ 13Generated tokens
COURSE 12 — Advanced Kernel Engineering
This becomes your Frontier AI + Systems level.
Modules
101. Warp specialization 202. Cooperative groups 303. asynchronous copies 404. TMA concepts 505. Tensor Cores 606. WMMA 707. MMA 808. fused kernels 909. persistent kernels 1010. CUDA Graphs 1111. Triton 1212. CUDA extensions for PyTorch 1313. custom autograd kernels 1414. distributed kernels 1515. NCCL 1616. multi-GPU memory 1717. CPU-GPU synchronization 1818. performance modeling 1919. roofline analysis 2020. kernel benchmarking
NVIDIA's current CUDA programming documentation covers the CUDA compilation workflow, kernels, thread hierarchy, memory hierarchy and asynchronous SIMT programming, making these appropriate advanced sections for the course. (NVIDIA Docs)
The most important connection
I would structure the whole curriculum around this single chain:
1C 2│ 3├── pointers 4├── structs 5├── malloc 6└── memory 7 ↓ 8Assembly 9│ 10├── registers 11├── stack 12├── instructions 13└── CPU 14 ↓ 15Linux 16│ 17├── process 18├── syscall 19├── ELF 20└── kernel 21 ↓ 22Virtual Memory 23│ 24├── page table 25├── MMU 26├── TLB 27└── physical memory 28 ↓ 29Linux Kernel 30│ 31├── scheduler 32├── filesystem 33├── driver 34└── kernel module 35 ↓ 36GPU 37│ 38├── CUDA 39├── threads 40├── warps 41├── blocks 42└── SM 43 ↓ 44GPU Memory 45│ 46├── registers 47├── shared memory 48├── L2 49└── VRAM 50 ↓ 51CUDA Kernels 52│ 53├── GEMM 54├── Softmax 55├── RMSNorm 56└── Attention 57 ↓ 58Transformer Kernels 59│ 60├── FlashAttention 61├── GQA 62├── RoPE 63├── MoE 64└── KV Cache 65 ↓ 66Frontier AI Systems
Two specialization tracks
🛡️ Cybersecurity Kernel Track
1C Internals 2 ↓ 3Assembly 4 ↓ 5Linux 6 ↓ 7Syscalls 8 ↓ 9ELF 10 ↓ 11Virtual Memory 12 ↓ 13Kernel 14 ↓ 15Drivers 16 ↓ 17Kernel Modules 18 ↓ 19Kernel Security 20 ↓ 21Fuzzing 22 ↓ 23Vulnerability Analysis 24 ↓ 25Defensive Kernel Engineering
🤖 AI Kernel Track
1C/C++ 2 ↓ 3CPU Architecture 4 ↓ 5CUDA 6 ↓ 7GPU Architecture 8 ↓ 9Memory Hierarchy 10 ↓ 11SIMT/Warp 12 ↓ 13CUDA Kernels 14 ↓ 15GEMM 16 ↓ 17Tensor Cores 18 ↓ 19Transformer Kernels 20 ↓ 21Attention 22 ↓ 23FlashAttention 24 ↓ 25KV Cache 26 ↓ 27Quantization 28 ↓ 29LLM Inference 30 ↓ 31Multi-GPU/NCCL
Recommended course project ladder
| Level | Project | AI | Cyber |
|---|---|---|---|
| 1 | Mini malloc | ⭐⭐ | ⭐⭐⭐⭐⭐ |
| 2 | ELF analyzer | ⭐⭐ | ⭐⭐⭐⭐⭐ |
| 3 | Mini shell | ⭐⭐ | ⭐⭐⭐⭐ |
| 4 | Virtual-memory simulator | ⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ |
| 5 | Linux kernel module | ⭐⭐ | ⭐⭐⭐⭐⭐ |
| 6 | Character device driver | ⭐⭐ | ⭐⭐⭐⭐⭐ |
| 7 | CUDA vector kernel | ⭐⭐⭐⭐⭐ | ⭐ |
| 8 | CUDA GEMM | ⭐⭐⭐⭐⭐ | ⭐ |
| 9 | Tensor Core GEMM | ⭐⭐⭐⭐⭐ | ⭐ |
| 10 | RMSNorm/Softmax kernels | ⭐⭐⭐⭐⭐ | ⭐ |
| 11 | Attention kernel | ⭐⭐⭐⭐⭐ | ⭐ |
| 12 | FlashAttention-style kernel | ⭐⭐⭐⭐⭐ | ⭐ |
| 13 | KV-cache engine | ⭐⭐⭐⭐⭐ | ⭐⭐ |
| 14 | LLM inference engine | ⭐⭐⭐⭐⭐ | ⭐⭐ |
| 15 | Kernel security lab | ⭐ | ⭐⭐⭐⭐⭐ |
Final outcome
If you complete this as a single serious curriculum, it is much stronger than a generic "CUDA course" because it connects:
C → Assembly → CPU → Linux → Kernel → Memory → GPU → CUDA → AI kernels → Transformer internals.
That gives you two complementary skill profiles:
Kernel/Systems + Cybersecurity and GPU Kernel/AI Systems.
For the AI side, CUDA is particularly relevant because its programming model is explicitly designed around massively parallel execution, thread groups, synchronization and memory hierarchy—the exact mechanisms that optimized deep-learning kernels exploit. (NVIDIA Docs)
For your existing roadmap, I would make Courses 1–7 the systems/cyber foundation and Courses 8–12 the GPU/Frontier-AI specialization, rather than treating kernel programming as only a cybersecurity topic.