readable-cpp

name	readable-cpp
description	Readable C/C++/Rust/CUDA code rules inspired by The Art of Readable Code. Use when writing, reviewing, or refactoring C, C++, Rust, or CUDA code. Enforces short functions, flat control flow, clear naming, readable structure, and idiomatic patterns.

Readable C/C++/Rust/CUDA Rules (/readable-cpp)

Apply these rules when writing, reviewing, or refactoring C, C++, Rust, or CUDA code. Inspired by The Art of Readable Code by Dustin Boswell and Trevor Foucher.

Core principle: Code should be easy to understand. The time it takes someone else (or future you) to understand the code is the ultimate metric.

1. Keep Functions Short and Focused

• A function should do one thing. If you can describe what it does with "and", split it.
• Aim for functions that fit on one screen (~15-25 lines). If it's longer, extract sub-tasks.
• Each function should operate at a single level of abstraction — don't mix high-level logic with low-level details in the same function.

2. Flatten Control Flow — No Deep Nesting

• Never nest more than 2 levels deep. If you have a loop inside a loop, or an if inside a loop inside an if, extract the inner block into a helper function with a descriptive name.
• Use early returns / guard clauses to handle edge cases at the top, keeping the main logic flat.
• Prefer continue or break to skip iterations rather than wrapping the body in a conditional.
• Replace complex conditionals with well-named helper functions or variables that explain the intent.

// Bad: nested and hard to follow
for (auto& user : users) {
    if (user.is_active()) {
        for (auto& order : user.orders()) {
            if (order.is_pending()) {
                process(order);
            }
        }
    }
}

// Good: flat, each function name explains what it does
auto active_users = get_active_users(users);
for (auto& user : active_users) {
    process_pending_orders(user.orders());
}

3. Name Things Clearly

• Pack information into names. Use specific, concrete words — fetch_page not get, num_retries not n.
• Avoid generic names like tmp, data, result, val, info, handle — unless the scope is tiny (2-3 lines).
• Use names that can't be misconstrued. If a range is inclusive, say max_items not limit. If a boolean, use is_, has_, should_, can_ prefixes.
• Match the name length to the scope. Short names for small scopes, descriptive names for wide scopes.
• Don't use abbreviations unless they're universally understood (num, max, min, err are fine; svc_mgr_cfg is not).

4. Make Control Flow Easy to Follow

• Put the changing/interesting value on the left side of comparisons: if (length > 10) not if (10 < length).
• Order if/else blocks: positive case first, simpler case first, or the more interesting case first.
• Minimize the number of variables the reader has to track. Reduce the mental footprint of each block.
• Avoid deeply nested ternary operators — if it's not immediately obvious, use an if/else.

5. Break Down Giant Expressions

• Use explaining variables to break complex expressions into named pieces.
• Use summary variables to capture a long expression that's used more than once.
• Apply De Morgan's laws to simplify negated boolean expressions.

// Bad
if (!(age >= 18 && has_id && !is_banned)) {
    deny();
}

// Good
bool is_eligible = age >= 18 && has_id && !is_banned;
if (!is_eligible) {
    deny();
}

6. Extract Unrelated Subproblems

• If a block of code is solving a subproblem unrelated to the main goal of the function, extract it.
• The helper function should be pure and self-contained — it shouldn't need to know about the calling context.
• This is the single most effective way to improve readability: separate what you're doing from how.

7. One Task at a Time

• Each section of code should do one task. If a function is doing parsing AND validation AND transformation, split them into separate steps.
• List the tasks a function does. If there's more than one, reorganize so each task is in its own block or function.

8. Reduce Variable Scope

• Declare variables close to where they're used. Don't declare at the top of a function if it's only used 30 lines later.
• Minimize the "live time" of a variable — the fewer lines between its assignment and last use, the easier it is to follow.
• Prefer write-once variables. Variables that are assigned once and never modified are easier to reason about.
• Eliminate unnecessary variables. If a variable is used only once and doesn't clarify anything, inline it.

9. No Magic Numbers or Strings

• Replace magic numbers and strings with named constants: if (retries > MAX_RETRIES) not if (retries > 3).
• If a value has meaning, give it a name. The name documents the intent.
• Group related constants together.

10. Fewer Function Arguments

• Aim for 3 or fewer arguments per function. More than that is a smell.
• Group related arguments into a struct, class, or tuple.
• If a function needs many config-like options, pass a single config/options object.
• Boolean flag arguments are a sign the function does two things — split it instead.

11. Consistency

• If the codebase does something one way, do it the same way. Don't mix styles.
• Consistent naming patterns, consistent structure, consistent error handling.
• When joining an existing codebase, match the existing conventions even if you'd prefer a different style.
• Surprise is the enemy of readability — predictable code is readable code.

12. Write Less Code

• The best code is no code at all. Question whether a feature is truly needed before implementing.
• Don't over-engineer. Solve the problem at hand, not hypothetical future problems.
• Remove dead code. Commented-out code is dead code.
• Use standard libraries before writing custom solutions.

13. Comments: Explain Why, Not What

• Don't comment what the code does — the code already says that. Comment why it does it.
• Comment flaws and workarounds: // TODO:, // HACK:, // XXX: with explanation.
• Comment surprising behavior or non-obvious decisions — things where a reader would ask "why?".
• Don't comment bad code — rewrite it. If you need a comment to explain what a block does, extract it into a well-named function instead.

14. Design Code to Survive Auto-Formatting

• Write code that looks good after the auto-formatter runs. If a chained expression or repeated pattern would be broken across 4+ lines by the formatter, extract a helper function instead.
• Prefer one-line helper calls over long inline chains that the formatter will expand vertically.
• The formatter is your reader's first impression. Run it before committing — if the result looks ugly, that's a signal to refactor, not to disable the formatter.

// Bad: rustfmt expands this to 4 lines per field — noisy and repetitive
fn from_dict(cfg: &Bound<'_, PyDict>) -> PyResult<Self> {
    Ok(Self {
        rom: cfg.get_item("rom")?.ok_or_else(|| missing("rom"))?.extract()?,
        // ... each field becomes 4 lines after rustfmt
    })
}

// Good: extract a helper so each field stays one clean line
fn get_required<T: FromPyObject>(cfg: &Bound<'_, PyDict>, key: &str) -> PyResult<T> {
    cfg.get_item(key)?
        .ok_or_else(|| PyKeyError::new_err(key.to_string()))?
        .extract()
}

fn from_dict(cfg: &Bound<'_, PyDict>) -> PyResult<Self> {
    Ok(Self {
        rom: get_required(cfg, "rom")?,
        actions: get_required(cfg, "actions")?,
    })
}

// Bad: clang-format wraps this into a hard-to-scan block
auto result = container.find(key)->second.get_value().transform(func).value_or(default_val);

// Good: name the intermediate step
auto& entry = container.find(key)->second;
auto result = entry.get_value().transform(func).value_or(default_val);

---

C-Specific Rules

15. RAII-Like Patterns with goto Cleanup

• In C, use the goto cleanup pattern for resource management — allocate at the top, clean up at a single labeled block at the bottom.
• Never scatter free() calls across multiple return paths. A single cleanup section is easier to audit.
• Use __attribute__((cleanup)) (GCC/Clang) when available for automatic cleanup.

// Good: single cleanup path
int process_file(const char *path) {
    int ret = -1;
    FILE *fp = fopen(path, "r");
    if (!fp) return -1;

    char *buf = malloc(BUF_SIZE);
    if (!buf) goto cleanup_file;

    // ... do work ...
    ret = 0;

cleanup_buf:
    free(buf);
cleanup_file:
    fclose(fp);
    return ret;
}

16. Use const Liberally

• Mark pointers const when the function doesn't modify the pointed-to data: const char *msg.
• Mark local variables const when they don't change after initialization.
• This documents intent and helps the compiler catch mistakes.

17. Prefer Sized Types for Data Structures

• Use <stdint.h> types (uint32_t, int64_t) for data that crosses boundaries (files, network, hardware).
• Use size_t for sizes and counts, ptrdiff_t for pointer differences.
• Use int and unsigned for simple loop counters and local arithmetic.

18. Defensive Macro Hygiene

• Wrap macro bodies in do { ... } while(0) for statement-like macros.
• Parenthesize all macro parameters: #define SQUARE(x) ((x) * (x)).
• Prefer static inline functions over macros when possible (type safety, debuggability).
• Use _Generic (C11) for type-safe "overloading" instead of macro tricks.

---

C++-Specific Rules

19. Use RAII for All Resources

• Every resource (memory, file handles, locks, sockets) should be owned by an RAII object.
• Use std::unique_ptr for exclusive ownership, std::shared_ptr only when shared ownership is genuinely needed.
• Write custom RAII wrappers for non-standard resources (e.g., C library handles).
• Never use raw new/delete in application code — let smart pointers and containers handle it.

20. Prefer Value Semantics and Move

• Pass small objects by value, large objects by const&.
• Return objects by value — rely on RVO/NRVO and move semantics.
• Implement move constructors/assignment for types that own resources.
• Use std::move only when you truly want to transfer ownership — don't std::move from things you'll use again.

21. Use Modern C++ Over C Idioms

• Use std::array over C arrays, std::string over char*, std::vector over malloc/realloc.
• Use std::optional over sentinel values, std::variant over type-unsafe unions.
• Use range-based for loops: for (const auto& item : container).
• Use structured bindings (C++17): auto [key, value] = *map.begin();.
• Use std::format (C++20) or fmt::format over sprintf / string concatenation.

22. Templates: Keep It Simple

• Use concepts (C++20) to constrain templates — errors become readable.
• Prefer if constexpr over SFINAE when possible.
• Don't write template metaprogramming unless the benefit is clear and the team can maintain it.
• A non-template solution that's slightly less generic is often better than a template solution nobody understands.

23. Use constexpr and const Aggressively

• Mark functions constexpr when they can be evaluated at compile time.
• Use constexpr variables instead of #define for constants.
• Use const on member functions that don't modify state.
• consteval (C++20) for functions that must be compile-time evaluated.

24. Error Handling: Pick One Pattern

• Use exceptions for truly exceptional conditions, std::expected (C++23) or std::optional for expected failures.
• Don't mix error codes and exceptions in the same layer.
• If using exceptions, make them specific — derive from std::runtime_error, not std::exception.
• Use noexcept on functions that cannot throw (destructors, move operations).

---

Rust-Specific Rules

25. Embrace the Ownership Model

• Don't fight the borrow checker — redesign your data flow instead.
• Prefer passing references (&T, &mut T) over cloning. Clone only when ownership transfer is genuinely needed.
• Use lifetimes explicitly only when the compiler can't infer them — don't annotate unnecessarily.
• Prefer &str over String in function parameters when you don't need ownership.

26. Use Iterators and Combinators

• Prefer iterator chains (.iter().filter().map().collect()) over manual loops with indices.
• Use for item in &collection instead of for i in 0..collection.len().
• Use enumerate(), zip(), chain(), chunks() — the iterator API is rich.
• Avoid .unwrap() in production code — use ?, unwrap_or, unwrap_or_else, or pattern matching.

// Bad: manual indexing
let mut names = Vec::new();
for i in 0..users.len() {
    if users[i].is_active {
        names.push(users[i].name.clone());
    }
}

// Good: idiomatic iterator chain
let names: Vec<_> = users.iter()
    .filter(|u| u.is_active)
    .map(|u| u.name.clone())
    .collect();

27. Use Enums and Pattern Matching

• Use enum with data variants instead of class hierarchies or tagged unions.
• Use match exhaustively — the compiler ensures you handle all cases.
• Use if let / while let for single-variant matching instead of full match.
• Prefer Result<T, E> over panicking — make errors part of the type signature.

28. Leverage the Type System

• Use newtype wrappers (struct UserId(u64)) to prevent mixing up same-typed values.
• Use Option<T> instead of sentinel values or null pointers.
• Use #[must_use] on functions whose return values shouldn't be ignored.
• Prefer From/Into traits for type conversions over manual conversion functions.

29. Module Organization

• Keep pub surfaces small — expose only what's needed.
• Use pub(crate) for crate-internal visibility instead of full pub.
• Group related types and functions in modules — one concept per module.
• Re-export key types at the crate root for ergonomic imports.

---

CUDA-Specific Rules

30. Name Kernels and Device Functions Clearly

• Kernel names should describe what they compute, not that they're kernels: reduce_sum not kernel1 or myKernel.
• Use a consistent naming convention to distinguish execution spaces: e.g., reduce_sum_kernel for __global__, warp_reduce for __device__ helpers.
• Name grid/block dimension variables descriptively: threads_per_block, num_blocks not tpb, nb, or bare 256.

// Bad: opaque names, magic numbers
__global__ void k1(float *a, float *b, int n) {
    int i = blockIdx.x * 256 + threadIdx.x;
    if (i < n) b[i] = a[i] * 2.0f;
}
k1<<<(n+255)/256, 256>>>(d_in, d_out, n);

// Good: clear intent, named constants
constexpr int THREADS_PER_BLOCK = 256;

__global__ void scale_kernel(const float *input, float *output,
                             float scale_factor, int num_elements) {
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx < num_elements) {
        output[idx] = input[idx] * scale_factor;
    }
}

const int num_blocks = (num_elements + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK;
scale_kernel<<<num_blocks, THREADS_PER_BLOCK>>>(d_input, d_output, 2.0f, num_elements);

31. Separate Host Logic from Device Logic

• Keep host orchestration (memory allocation, transfers, kernel launches, synchronization) in separate functions from device computation (kernels and device helpers).
• Don't mix cudaMalloc/cudaMemcpy with application logic — wrap them in RAII classes or helper functions.
• Use a clear file organization: consider separating .cu kernel files from .cpp host logic files, or at minimum group host and device code into clearly labeled sections.

// Good: RAII wrapper hides allocation/deallocation
template <typename T>
class DeviceBuffer {
    T *ptr_ = nullptr;
    size_t size_ = 0;
public:
    explicit DeviceBuffer(size_t count) : size_(count) {
        check_cuda(cudaMalloc(&ptr_, count * sizeof(T)));
    }
    ~DeviceBuffer() { cudaFree(ptr_); }

    DeviceBuffer(const DeviceBuffer&) = delete;
    DeviceBuffer& operator=(const DeviceBuffer&) = delete;
    DeviceBuffer(DeviceBuffer&& o) noexcept : ptr_(o.ptr_), size_(o.size_) { o.ptr_ = nullptr; }

    T *get() { return ptr_; }
    const T *get() const { return ptr_; }
    size_t size() const { return size_; }

    void copy_from_host(const T *host_data) {
        check_cuda(cudaMemcpy(ptr_, host_data, size_ * (T), cudaMemcpyHostToDevice));
    }
    {
        ((host_data, ptr_, size_ * (T), cudaMemcpyDeviceToHost));
    }
};

32. Always Check CUDA Errors

• Check every CUDA API call. Silent failures are the #1 source of hard-to-debug CUDA issues.
• Use a check_cuda macro or inline function — not raw if blocks after every call.
• Check errors after kernel launches with cudaGetLastError() + cudaDeviceSynchronize() during development.
• In release builds, at minimum check allocations and memcpy — these are the most likely to fail at runtime.

// Good: concise, catches file/line info
inline void check_cuda(cudaError_t err, const char *file, int line) {
    if (err != cudaSuccess) {
        fprintf(stderr, "CUDA error at %s:%d — %s\n",
                file, line, cudaGetErrorString(err));
        exit(EXIT_FAILURE);
    }
}
#define check_cuda(err) check_cuda((err), __FILE__, __LINE__)

// Usage
check_cuda(cudaMalloc(&d_ptr, size));
my_kernel<<<grid, block>>>(d_ptr, n);
check_cuda(cudaGetLastError());
check_cuda(cudaDeviceSynchronize());

33. Make Thread Indexing Obvious

• Compute the global thread index once at the top of the kernel and store it in a clearly named variable.
• Use early return for out-of-bounds threads — don't wrap the entire kernel body in an if.
• For 2D/3D grids, name dimensions explicitly: row, col, depth — not x, y, z.

// Bad: index computed inline, entire body wrapped
__global__ void process(float *data, int width, int height) {
    if (blockIdx.x * blockDim.x + threadIdx.x < width &&
        blockIdx.y * blockDim.y + threadIdx.y < height) {
        int idx = (blockIdx.y * blockDim.y + threadIdx.y) * width +
                  (blockIdx.x * blockDim.x + threadIdx.x);
        data[idx] = data[idx] * 2.0f;
    }
}

// Good: named indices, early return
__global__ void process(float *data, int width, int height) {
    const int col = blockIdx.x * blockDim.x + threadIdx.x;
    const int row = blockIdx.y * blockDim.y + threadIdx.y;
    if (col >= width || row >= height) return;

    const int idx = row * width + col;
    data[idx] = data[idx] * 2.0f;
}

34. Document Shared Memory Usage

• Declare shared memory with a descriptive name that indicates what it holds: shared_tile not smem or s.
• Add a brief comment explaining the size and purpose of shared memory when it's dynamically allocated (extern __shared__).
• Keep the shared memory lifecycle short — load, sync, compute, sync — and make each phase visually distinct.

// Good: clear phases, descriptive names
__global__ void tiled_matmul_kernel(const float *A, const float *B,
                                     float *C, int N) {
    __shared__ float tile_A[TILE_SIZE][TILE_SIZE];
    __shared__ float tile_B[TILE_SIZE][TILE_SIZE];

    const int row = blockIdx.y * TILE_SIZE + threadIdx.y;
    const int col = blockIdx.x * TILE_SIZE + threadIdx.x;
    float accumulator = 0.0f;

    for (int tile_idx = 0; tile_idx < N / TILE_SIZE; ++tile_idx) {
        // Phase 1: Load tiles from global memory
        tile_A[threadIdx.y][threadIdx.x] = A[row * N + tile_idx * TILE_SIZE + threadIdx.x];
        tile_B[threadIdx.y][threadIdx.x] = B[(tile_idx * TILE_SIZE + threadIdx.y) * N + col];
        __syncthreads();

        // Phase 2: Compute partial dot product from tiles
        for (int k = 0; k < TILE_SIZE; ++k) {
            accumulator += tile_A[threadIdx.y][k] * tile_B[k][threadIdx.x];
        }
        __syncthreads();
    }

    C[row * N + col] = accumulator;
}

35. Keep Kernels Short — Extract Device Helpers

• Apply the same "one function, one task" rule to kernels. If a kernel does loading, computing, and reducing, extract __device__ helper functions.
• Use __forceinline__ __device__ for small helpers that you want inlined without relying on compiler heuristics.
• This makes kernels easier to read, test (via unit-testing device functions), and reuse.

// Good: kernel reads like pseudocode, details in helpers
__forceinline__ __device__
float warp_reduce_sum(float val) {
    for (int offset = warpSize / 2; offset > 0; offset /= 2) {
        val += __shfl_down_sync(0xffffffff, val, offset);
    }
    return val;
}

__forceinline__ __device__
float block_reduce_sum(float val) {
    __shared__ float warp_sums[32];
    const int lane = threadIdx.x % warpSize;
    const int warp_id = threadIdx.x / warpSize;

    val = warp_reduce_sum(val);
    if (lane == 0) warp_sums[warp_id] = val;
    __syncthreads();

    val = (threadIdx.x < blockDim.x / warpSize) ? warp_sums[lane] : 0.0f;
    if (warp_id == 0) val = warp_reduce_sum(val);
    return val;
}

__global__ void reduce_sum_kernel(const float *input, float *output, int n) {
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    const float val = (idx < n) ? input[idx] : 0.0f;

    const float block_sum = block_reduce_sum(val);
    if (threadIdx.x == 0) atomicAdd(output, block_sum);
}

36. Be Explicit About Memory Spaces

• Use const on kernel parameters for read-only device pointers — documents intent and enables compiler optimizations.
• Use __restrict__ when pointers don't alias — but add a comment explaining the non-aliasing guarantee.
• When using unified memory (cudaMallocManaged), comment the expected access pattern (host-only init, device-only compute, etc.) — the implicit page migration behavior is not obvious.
• Prefer explicit memory copies over unified memory in performance-critical paths — be explicit about data movement.

// Good: const + restrict with clear intent
__global__ void vector_add_kernel(
    const float *__restrict__ a,   // read-only, no alias with output
    const float *__restrict__ b,   // read-only, no alias with output
    float *__restrict__ output,    // write-only
    int num_elements)
{
    const int idx = blockIdx.x * blockDim.x + threadIdx.x;
    if (idx >= num_elements) return;
    output[idx] = a[idx] + b[idx];
}

37. Synchronization: Make It Visible and Minimal

• Place __syncthreads() on its own line, never buried inside a conditional branch that not all threads take — this is undefined behavior and hard to spot.
• Add a brief comment before each __syncthreads() stating what invariant it establishes: "all threads have loaded their tile", "partial sums are written to shared memory".
• Minimize synchronization points — restructure algorithms to reduce the number of barriers.
• For warp-level operations, prefer warp intrinsics (__shfl_sync, __ballot_sync) with explicit masks over __syncthreads().

38. Launch Configuration: Make It Readable

• Wrap kernel launches in a host function that computes and names the launch parameters.
• Never hardcode grid/block dimensions at the call site — compute them from the problem size.
• For complex launch configurations, use a struct or helper to make the 2D/3D grid/block shape clear.
• Query device properties at startup rather than assuming specific hardware limits.

// Bad: magic numbers, unclear intent
foo<<<(n+127)/128, 128, 0, stream>>>(d_ptr, n);

// Good: named, computed, self-documenting
void launch_scale_kernel(float *d_data, float factor, int n, cudaStream_t stream) {
    constexpr int BLOCK_SIZE = 256;
    const int grid_size = (n + BLOCK_SIZE - 1) / BLOCK_SIZE;
    scale_kernel<<<grid_size, BLOCK_SIZE, 0, stream>>>(d_data, factor, n);
    check_cuda(cudaGetLastError());
}

39. Streams and Async: Comment the Dependency Graph

• When using multiple CUDA streams, add a comment block showing the dependency graph — which operations must complete before others begin.
• Name streams after their purpose: compute_stream, transfer_stream — not s1, s2.
• Group related async operations visually and separate independent pipelines with blank lines.
• Always synchronize before reading results on the host — make the sync point explicit and commented.

// Good: dependency graph documented, streams named by purpose
// Dependency graph:
//   upload (transfer_stream) --> compute (compute_stream) --> download (transfer_stream)
//   Event 'upload_done' gates compute start.
//   Event 'compute_done' gates download start.

cudaStream_t transfer_stream, compute_stream;
cudaEvent_t upload_done, compute_done;

// Stage 1: async upload
cudaMemcpyAsync(d_input, h_input, size, cudaMemcpyHostToDevice, transfer_stream);
cudaEventRecord(upload_done, transfer_stream);

// Stage 2: compute waits for upload
cudaStreamWaitEvent(compute_stream, upload_done);
process_kernel<<<grid, block, 0, compute_stream>>>(d_input, d_output, n);
cudaEventRecord(compute_done, compute_stream);

// Stage 3: download waits for compute
cudaStreamWaitEvent(transfer_stream, compute_done);
cudaMemcpyAsync(h_output, d_output, size, cudaMemcpyDeviceToHost, transfer_stream);

// Sync before host reads the result
cudaStreamSynchronize(transfer_stream);