layout: true class: typo, typo-selection --- count: false class: nord-dark, middle, center # 🗺️ Generic vs CSR ⚡ ## Network Optimization: A Performance Comparison @luk036 👨‍💻 · 2026 📅 --- ### 📋 Agenda .pull-left[ **Part 1: Background** 🧠 - Motivation & Problem - Graph Representations - Howard's Algorithm **Part 2: digraphx Benchmark** 🔬 - NegCycleFinder (Howard) - Results at Every Scale ] .pull-right[ **Part 3: netoptim Benchmark** 📊 - NetworkOracle - Results at Every Scale **Part 4: Analysis** 🎯 - Why CSR Wins - Key Takeaways ] --- class: nord-light, middle, center ## 🧠 Motivation & Background --- ### The Problem: Graph Representation Matters 📐 Network optimization algorithms operate on **large directed graphs**: $$ {\color{salmon}G} = ({\color{salmon}V}, {\color{lime}E}), \quad |{\color{salmon}V}| \gg 10^5 $$ The core operation is **edge relaxation**: $$ {\color{coral}d_v} \gets \min({\color{coral}d_v},\; {\color{coral}d_u} + {\color{coral}w_{uv}}), \quad \forall (u,v) \in {\color{lime}E} $$ The perf difference between hash-map and CSR grows with the graph: .mermaid[ <pre> graph TD subgraph "Generic (Hash-map)" A1["node u"] -->|"hash lookup 💸"| B1["neighbors map"] B1 -->|"hash lookup 💸"| C1["edge w_uv"] end subgraph "CSR" A2["offsets[u]"] -->|"direct index ✅"| B2["targets[e]"] B2 -->|"direct index ✅"| C2["weights[e]"] end style A1 fill:#fce4ec,stroke:#ad1457,stroke-width:3px style B1 fill:#fce4ec,stroke:#ad1457,stroke-width:3px style C1 fill:#fce4ec,stroke:#ad1457,stroke-width:3px style A2 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style B2 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style C2 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px </pre> ] --- ### Two Codebase Families 🏗️ .pull-left[ **digraphx-cpp** 🧩 - Generic hash-map graphs - `absl::flat_hash_map` adjacency - Python-style iterators - Heavily templated *Built for flexibility* ] .pull-right[ **digraphx-fast** 🚀 - CSR (Compressed Sparse Row) - `std::vector` arrays - Cache-friendly iteration - Pre-allocated working memory *Built for speed* ] Each has a higher-level sibling: `netoptim-cpp` and `netoptim-fast`. --- ### How CSR Works 📊 CSR stores edges in **three flat arrays**: $$ {\color{green}\text{offsets}}[u] \to {\color{green}\text{targets}}[e],\; {\color{green}\text{weights}}[e] $$ .mermaid[ <pre> graph TD subgraph "Node 0" O0["offsets[0]=0"] end subgraph "Node 1" O1["offsets[1]=2"] end subgraph "Node 2" O2["offsets[2]=4"] end subgraph "Targets" T["[1, 2, 0, 2, 0, 1]"] end subgraph "Weights" W["[10, 20, 30, 40, 50, 60]"] end O0 -.->|"edges 0-1"| T O1 -.->|"edges 2-3"| T O2 -.->|"edges 4-5"| T style O0 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style O1 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style O2 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style T fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px style W fill:#f3e5f5,stroke:#7b1fa2,stroke-width:3px </pre> ] ✅ **Cache-friendly**: Contiguous memory, predictable access patterns ✅ **Zero allocation**: Pre-allocated arrays, no hash probes ✅ **SIMD-friendly**: Sequential scan over edges --- ### Howard's Algorithm 🔄 The core algorithm benchmarked is **Howard's policy iteration** for negative cycle detection: $$ \text{Relax all edges} \to \text{Find cycle in predecessor graph} \to \text{Verify negativity} $$ .mermaid[ <pre> graph LR reset["Reset\npredecessor map"] --> relax["Relax all edges\nO(E)"] relax --> changed{"Changed?"} changed -->|"Yes"| cycle["Find cycle in\npredecessor graph\nO(V)"] changed -->|"No"| done["Return\nfound > 0"] cycle --> neg{"Negative?"} neg -->|"Yes"| yield["Yield cycle\nvia callback"] neg -->|"No"| relax yield --> relax style reset fill:#fce4ec,stroke:#ad1457,stroke-width:3px style relax fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style changed fill:#fff9c4,stroke:#f57f17,stroke-width:3px style cycle fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style neg fill:#fff9c4,stroke:#f57f17,stroke-width:3px style yield fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px style done fill:#e3f2fd,stroke:#1565c0,stroke-width:3px </pre> ] --- class: nord-light, middle, center ## 🔬 Benchmark Methodology --- ### Benchmark Setup 🔧 | Parameter | Value | |-----------|-------| | **Compiler** | MSVC 2026, `/O2 -GL -LTCG` | | **Build System** | XMake, release mode | | **Graph Model** | Ring graph, ${\color{coral}k}=3$ neighbors | | **Negative Cycle** | ${\color{lime}0 \to 1 \to 2 \to 0}$, ${\color{coral}w} = -5$ each | | **Runs** | 5 per configuration | | **System** | Windows x64, GCC 15.2.0 | **Verification**: Both implementations find the **same cycle** with **same total weight** ${\color{firebrick}-15}$ ✅ --- ### Ring Graph Topology 📐 Each node connects to ${\color{coral}k} = 3$ forward neighbors with positive pseudo-random weights: $$ {\color{coral}w_{ij}} = ((i+1) \times 7 + (j+1) \times 13) \bmod 100 + 1 $$ Plus three forced negative edges forming cycle ${\color{lime}0 \to 1 \to 2 \to 0}$: .mermaid[ <pre> graph LR 0 -->|"-5"| 1 1 -->|"-5"| 2 2 -->|"-5"| 0 0 -->|"w_01"| 1 0 -->|"w_02"| 2 0 -->|"w_03"| 3 1 -->|"w_12"| 2 1 -->|"w_13"| 3 1 -->|"w_14"| 4 2 -->|"w_23"| 3 2 -->|"w_24"| 4 2 -->|"w_25"| 5 style 0 fill:#ffcdd2,stroke:#c62828,stroke-width:3px style 1 fill:#ffcdd2,stroke:#c62828,stroke-width:3px style 2 fill:#ffcdd2,stroke:#c62828,stroke-width:3px </pre> ] --- class: nord-light, middle, center ## 📊 digraphx Benchmark Results --- ### NegCycleFinder: Howard's Algorithm 🏁 **digraphx-cpp** (hash-map) vs **digraphx-fast** (CSR) | Nodes | Edges | Hash-map (ms) | CSR (ms) | **Speedup** | |------:|------:|--------------:|---------:|:----------:| | 20,000 | 60,003 | 2.03 | 0.25 | **8.1×** | | 50,000 | 150,003 | 6.72 | 0.64 | **10.5×** | | 100,000 | 300,003 | 10.30 | 1.25 | **8.2×** | | 200,000 | 600,003 | 26.30 | 2.06 | **12.8×** | | 500,000 | 1,500,003 | 91.35 | 6.83 | **13.4×** | | **1,000,000** | **3,000,003** | **176.90** | **10.35** | **17.1×** | Both find the same negative cycle ✅ (weight = ${\color{firebrick}-15}$) --- ### Speedup Trend 📈 ``` Speedup vs Graph Size (NegCycleFinder) 20× ┤ 18× ┤ ╱── 17.1× 16× ┤ ╱╱ 14× ┤ ╱╱─── 13.4× 12× ┤ ╱╱─── 12.8× 10× ┤ ╱╱──── 10.5× 8× ┤ ╱╱── 8.1× 6× ┤ 4× ┤ 2× ┤ 0× ┼──────────────────────────────────────── 20k 50k 100k 200k 500k 1M ``` **Observation**: The speedup **grows with graph size** — CSR scales near-linearly, hash-map scales super-linearly. --- ### Scaling Analysis 📉 **digraphx-cpp** (hash-map): $$ T(n) \propto O\left(\frac{{\color{coral}V} \cdot {\color{coral}E}}{\text{cache\_line}}\right) + \text{hash\_overhead} $$ - Each relaxation pass: ${\color{coral}n}$ hash map lookups for distances - ${\color{coral}E}$ hash map lookups for neighbors and edge data - ${\color{coral}V}$ hash map insertions for predecessor updates - Cycle finding: hash map for visited tracking **digraphx-fast** (CSR): $$ T(n) \propto O({\color{coral}V} + {\color{coral}E}) $$ - Distance: `vector<double>` direct index - Neighbors: `targets[e]` direct array access - Weights: `weights[e]` direct array access - Visited: `vector<node_t>` with epoch sentinel — **no `clear()` call** --- class: nord-light, middle, center ## 📊 netoptim Benchmark Results --- ### NetworkOracle: Separation Oracle 🏁 **netoptim-cpp** (hash-map) vs **netoptim-fast** (CSR) | Nodes | Edges | Hash-map (ms) | CSR (ms) | **Speedup** | |------:|------:|--------------:|---------:|:----------:| | 20,000 | 60,003 | 25.96 | 0.84 | **31×** | | 50,000 | 150,003 | 55.19 | 1.85 | **30×** | | 100,000 | 300,003 | 140.32 | 3.24 | **43×** | | 200,000 | 600,003 | 277.34 | 6.87 | **40×** | | 500,000 | 1,500,003 | 824.61 | 14.48 | **57×** | | **1,000,000** | **3,000,003** | **1713.21** | **30.20** | **57×** | Both return the **same cutting plane** ✅ --- ### Speedup Trend 📈 ``` Speedup vs Graph Size (NetworkOracle) 60× ┤ ╱── 57× 50× ┤ ╱╱─── 57× 40× ┤ ╱╱── 43× 30× ┤ ╱╱─── 40× ┆ ╱╱── 31× ╱╱── 30× 20× ┤ 10× ┤ 0× ┼──────────────────────────────────────── 20k 50k 100k 200k 500k 1M ``` The gap **widens dramatically** compared to raw NegCycleFinder! At 1M nodes: **57× speedup**. --- ### Why the Gap is Wider 🔬 The `NetworkOracle` wrapper **amplifies** the differences: | Overhead | Hash-map | CSR | |----------|----------|-----| | **Distance init** | `flat_hash_map::operator[]` ${\color{coral}n}$ times | `vector<double>(n, 0.0)` | | **Weight per edge** | Callable + hash lookups | Pre-computed `vector` | | **Predecessor map** | `flat_hash_map` insert_or_assign | Pre-allocated `vector` | | **Visited tracking** | `flat_hash_map` reserve + contains | `vector` + epoch sentinel | | **Cycle extraction** | New vector allocation per call | Reference to internal buffer | Each of these is individually faster in CSR; **together they compound**. --- class: nord-light, middle, center ## 🎯 Why CSR Wins --- ### Memory Access Patterns 🧠 Hash-map access is **random** — each lookup may miss cache: $$ P(\text{cache\_hit}) \approx \frac{\text{cache\_size}}{{\color{coral}n} \cdot \text{sizeof(entry)}} $$ CSR access is **sequential** — prefetcher-friendly: .mermaid[ <pre> graph TD subgraph "CSR Memory Layout" M1["[w₀ w₁ w₂ ... wₑ] ──▶ sequential scan"] end subgraph "Hash-map Memory Layout" M2["[w₀ ████ w₂ ██ w₁ ...] ──▶ random access"] end style M1 fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style M2 fill:#fce4ec,stroke:#ad1457,stroke-width:3px </pre> ] - **CSR**: $O({\color{coral}E})$ sequential reads, $O({\color{coral}V})$ sequential writes - **Hash-map**: $O({\color{coral}E})$ random reads (cache misses ~ 100ns each), $O({\color{coral}V})$ random writes At $10^6$ edges, that's $10^6$ potential cache misses $\approx$ **100ms wasted** 💸 --- ### Allocation Overhead 📦 Howard's algorithm in hash-map allocates on **every iteration**: ```text Per howard() call: - flat_hash_map for dist (n entries) - flat_hash_map for _pred (n entries) - flat_hash_map for visited (n entries) - vector for cycle (variable) ``` Per howard() call in CSR: ```text - vector<weight_t> weights (pre-allocated) - vector<node_t> pred_node (pre-allocated) - vector<node_t> visited (pre-allocated) - vector<size_t> cycle (reused buffer) ``` **Allocation + deallocation is the hidden tax.** 💰 --- ### Epoch-Based Visited Tracking ⏱️ Hash-map visited: **O(V)** per call to `clear()` + re-allocation: $$ \text{Cost} = \underbrace{O({\color{coral}V})}_{\text{rehash}} + \underbrace{O({\color{coral}V})}_{\text{re-insert}} $$ CSR epoch visited: **O(1)** — just increment a counter: ```cpp // hash-map version auto visited = absl::flat_hash_map<Node, Node>{}; visited.reserve(this->_digraph.size()); // O(V) alloc // CSR version std::fill(_visited.begin(), _visited.end(), node_t(-1)); // O(V) memset ``` No re-allocation, no rehashing. Just write `-1` over a contiguous array. --- class: nord-light, middle, center ## 🎯 Key Takeaways --- ### Summary Table 📋 | Aspect | Generic (Hash-map) | CSR | |--------|:------------------:|:---:| | **Flexibility** | ✅ Any key type | ⚠️ Integer nodes | | **Memory** | ❌ Fragmented | ✅ Contiguous | | **Cache** | ❌ Random access | ✅ Sequential | | **Allocations** | ❌ Per-iteration | ✅ Pre-allocated | | **Speed (1M nodes)** | 177 ms | **10 ms** | | **Speedup vs Generic** | 1× | **17× (raw)** / **57× (oracle)** | --- ### When to Use Which 🤔 **Use generic (hash-map) when:** - Graphs are small ($|{\color{coral}V}| < 10^4$) - Node IDs are sparse or non-integer - Development speed matters more than runtime - Graph structure changes frequently **Use CSR when:** - Graphs are large ($|{\color{coral}V}| > 10^4$) - Performance-critical inner loop - Static or incrementally-built graphs - Cache efficiency is a concern > 💡 **Rule of thumb**: If your graph takes > 1ms to process, CSR will pay off. --- ### Key Insights 💡 1. **CSR is 8–17× faster** for raw Howard's algorithm 2. **CSR is 30–57× faster** for the NetworkOracle wrapper 3. The **gap widens with graph size** — cache effects compound 4. **Pre-allocation is the killer feature** — no per-iteration `malloc` 5. **Epoch-based visited tracking** eliminates `clear()` overhead **Bottom line**: Graph representation choice is the single biggest performance lever in network optimization. 🎯 --- ### Future Directions 🚀 - **Parallel relaxation**: CSR's contiguous arrays are GPU-friendly - **SIMD vectorization**: Process 4–8 edges per instruction - **Hybrid approach**: CSR for hot paths, hash-map for dynamic updates - **Arena allocation**: Reuse memory pools even in hash-map version .mermaid[ <pre> graph TD subgraph "Future Architecture" A["CSR Graph\n(fast paths)"] --> B["Howard's\nAlgorithm"] C["Hash Index\n(mutation)"] -.->|"rebuild"| A end style A fill:#e3f2fd,stroke:#1565c0,stroke-width:3px style B fill:#c8e6c9,stroke:#2e7d32,stroke-width:3px style C fill:#fce4ec,stroke:#ad1457,stroke-width:3px </pre> ] --- ### References 📚 | Reference | Link | |-----------|------| | digraphx-cpp | [github.com/luk036/digraphx-cpp](https://github.com/luk036/digraphx-cpp) | | digraphx-fast | [github.com/luk036/digraphx-fast](https://github.com/luk036/digraphx-fast) | | netoptim-cpp | [github.com/luk036/netoptim-cpp](https://github.com/luk036/netoptim-cpp) | | netoptim-fast | [github.com/luk036/netoptim-fast](https://github.com/luk036/netoptim-fast) | | Howard's Algorithm | Cochet-Terrasson et al. (1994) | | CSR Graph | Saad, Y. (2003). *Iterative Methods for Sparse Linear Systems* | --- count: false class: nord-dark, middle, center ## 🙏 Thank You! 🎤 ### Questions? Slides: luk036.github.io/net_optim/generic-vs-csr-remark.html