Reading the following blog:

Knuth’s “premature optimization” quote. While 97% of code requires no optimization, neglecting the critical 3% (core libraries, hot paths, inner loops) results in technical debt that is hard to reverse.

  • The “Flat Profile” Trap: If performance is ignored during early development, the resulting system often exhibits a “flat profile”—CPU usage is spread evenly across thousands of inefficient operations (allocations, copies, cache misses). There is no single hotspot to fix, forcing engineers to make hundreds of micro-optimizations later.

  • Back-of-the-Envelope Estimation: Before implementing complex designs, engineers should estimate latency using resource costs. For example, estimating Quicksort speed by calculating memory bandwidth ($N \times \text{passes}$) versus branch misprediction costs ($N \log N \times \text{mispredict rate}$).

Hardware Sympathy (2025 Latency Numbers)

Optimization decisions must be grounded in the latency of modern hardware. The 2025 numbers highlight the massive penalty of leaving the CPU cache.

Operation Latency (approx.) Insight
L1 Cache Ref 0.5 ns Keep data here.
Mutex Lock (uncontended) 15 ns Expensive in tight loops.
RAM Reference 50 ns ~100x slower than L1. Cache locality is king.
Compress 1KB (Snappy) 1,000 ns Compute is often cheaper than I/O.
Read 1MB (SSD) 1,000,000 ns 1 ms. Avoid blocking on I/O.
Packet CA->Netherlands 150,000,000 ns 150 ms. Network is the ultimate bottleneck.

Data Structure Selection

It advocates replacing standard STL containers with “hardware-aware” alternatives that prioritize cache locality and reduce pointer chasing.

A. Maps and Sets

  • Swiss Tables (absl::flat_hash_map):
    • Mechanism: Uses open addressing and SIMD instructions (SSE2/AVX) to compare 16 hash bytes in parallel (metadata) before checking keys.
    • Benefit: 47% speedup observed in CodeToLanguage lookup. Avoids the node-allocation overhead of std::unordered_map.
  • B-Trees (absl::btree_map/set):
    • Mechanism: Stores multiple keys per node.
    • Benefit: Reduces pointer chasing compared to Red-Black trees (std::map). Better cache line utilization.
  • Small/Hybrid Maps:
    • gtl::small_map: Stores small N (e.g., <10) inline in an array; upgrades to a hash map only when full.
    • Replacement: Replaced std::set with absl::btree_set for work queues to reduce allocator thrashing.

B. Vectors and Indices

  • absl::InlinedVector: Stores small N directly in the object (stack allocation) rather than on the heap. Critical for “hot” temporary vectors.
  • Indices vs. Pointers: On 64-bit machines, pointers are 8 bytes. Using uint32 indices into a flat array saves 50% memory and improves spatial locality.
  • Bit Vectors: Use util::bitmap::InlinedBitVector instead of std::vector<bool> or hash_set<int> for dense integer sets. Enables bitwise logic (OR/AND) for set operations.

Reducing Allocations (The “Silent Killer”)

Allocations incur allocator lock contention and scatter data across memory (bad locality).

  • Hoisting: Move temporary objects (protos, strings, vectors) outside loops. Use Clear() to reuse the capacity.
  • Pre-sizing: Use reserve() on vectors. Avoid resize() for complex types as it default-constructs N objects.
  • Arenas: Use Arena allocation (especially for Protobufs) to pack unrelated objects into contiguous blocks and enable $O(1)$ bulk deallocation.

Protocol Buffers (Protobuf) Optimization

Protobufs are convenient but computationally expensive (up to 20x slower than structs for arithmetic ops).

  • Type Selection:
    • Use bytes over string for binary data to skip UTF-8 validation.
    • Use [ctype=CORD] for large blobs to prevent linear copying during parsing/serialization.
    • Use fixed32 over int32 for large values (hashes, random IDs) where Varint encoding is inefficient.
  • Structure:
    • Avoid deep nesting (requires recursive allocation/parsing).
    • Don’t use Proto Maps (map<string, val>); they are inefficient. Prefer repeated messages.
  • Parsing:
    • Partial Parsing: If only a few fields are needed, define a “SubsetMessage” proto containing only those tags to skip parsing the rest.
    • View API: Use [string_type=VIEW] to map fields to absl::string_view (pointing into the raw buffer) rather than copying to std::string.

Algorithmic Patterns

  • Graph Cycles: Inserting nodes in reverse post-order makes cycle detection trivial.
  • Deadlock Detection: Replaced an $O(|V|^2)$ algorithm with a dynamic topological sort ($O(|V|+|E|)$), enabling scale from 2k to millions of locks.
  • Hashing vs. Sorting: Replaced sorted-list intersection ($O(N \log N)$) with hash-table lookups ($O(N)$) for a 21% speedup.

Execution Efficiency: “Avoiding Work”

A. Fast Paths

Specialized logic for the common case, falling back to generic logic only when necessary.

  • Varint Parsing: Inline the check for < 128 (1-byte varint). Call a noinline slow function for multi-byte integers. This reduces instruction cache pressure.
  • ASCII Scanning: Check 8 bytes at a time for the high bit (0x80) to skip UTF-8 processing for ASCII blocks.

B. Precomputation & Lazy Evaluation

  • Lazy: Defer expensive calls like GetSubSharding until the operand is confirmed to be relevant.
  • Precompute: Pre-calculate lookup tables (e.g., 256-element array for trigrams) instead of computing log-probs in the loop.

C. Logging

  • Conditional VLOG: Logging logic (even disabled) adds branch overhead. Use if (VLOG_IS_ON(1)) to guard parameter preparation.
  • Code Bloat: ABSL_LOG(FATAL) generates significant assembly. For non-debug builds, consider ABSL_DCHECK(false) or outlining the error string generation.

Concurrency & Parallelism

  • Sharding Locks: A single mutex on a map limits throughput. Split the map into $N$ shards (e.g., by thread_id or hash(key)) with independent locks. This yielded a 69% reduction in wall-clock time for Spanner’s active call tracking.
  • Critical Sections: Never perform I/O or RPCs inside a lock. Precompute values outside the lock, acquire, swap pointers/update, and release immediately.
  • False Sharing: Use alignas(ABSL_CACHELINE_SIZE) to separate frequently written fields (like counters) to prevent cache line invalidation across cores.

Code Size and Compiler Optimizations

Large code causes instruction cache (i-cache) misses.

  • Outlining: Mark error paths and slow paths (like TF_CHECK_OK failure strings) as noinline or move them to separate functions.
  • Template Bloat: Convert template parameters to function arguments (e.g., template<bool> $\to$ bool arg) to prevent the compiler from generating duplicate binary code for every boolean permutation.
  • Loop Unrolling: Manually unroll loops (e.g., CRC32) only when benchmarks prove the compiler isn’t doing it effectively.

API Design for Performance

  • Bulk APIs: Replace Lookup(key) with LookupMany(keys). This amortizes the cost of virtual function calls and lock acquisition over a batch of items.
  • Thread-Compatibility: Design classes to be thread-compatible (external sync required) rather than thread-safe (internal locks). This avoids forcing single-threaded users to pay for atomic operations they don’t need.