Roofline Toolkit

• Roofline Toolkit is a performance modeling environment that automates both hardware and software characterization with empirical and analytic metrics.
• It integrates microbenchmarks, static analysis, and interactive visualization to diagnose compute and memory bottlenecks in various architectures.
• The Toolkit is extensible, supporting multi-metric analysis and cross-system comparisons via deep IDE integration for advanced performance tuning.

The Roofline Toolkit is a comprehensive performance modeling and visualization environment based on the Roofline Model, designed to streamline empirical and analytic performance analysis for CPUs and accelerators. Developed collaboratively by Argonne National Laboratory, Lawrence Berkeley National Laboratory, and the University of Oregon, it automates the process of hardware and software characterization, performance data collection, and interactive visualization, with extensibility toward multi-metric and cross-platform workflows (Spear et al., 2015).

1. Roofline Model Foundations

The Roofline Model formalizes the upper bound for performance ($P_k$) of a computational kernel, determined by both available compute throughput and memory subsystem bandwidth. It is encapsulated by

$I = \frac{W}{D}$

where $I$ is arithmetic intensity (FLOP per byte), $W$ the floating-point work, and $D$ the total data movement. The model posits two primary “roofs”:

• Memory bandwidth roof: $P_\text{mem} = \beta I$, $\beta$ peak bandwidth (bytes/s)
• Compute roof: $P_\text{peak}$, the peak FLOPS achievable by the architecture

The tight bound on achievable performance is thus

$P_\text{max} = \min(P_\text{mem}, P_\text{peak})$

Graphically, this produces a log–log chart with a horizontal compute roof intersected by a sloped memory roof at $I^* = P_\text{peak}/\beta$.

2. Toolkit Architecture and Workflow

The Toolkit features three primary subsystems (Spear et al., 2015):

• Hardware Characterization:
  • Portable microbenchmarks measure peak bandwidth (L1, L2, DRAM) and compute throughput.
  • Broad support for CPUs and accelerators; empirical bandwidths and FLOP rates are used as model ceilings.
• Software Characterization:
  • Static analysis (source/binary) and empirical measurements (e.g., TAU) estimate kernel arithmetic intensity and actual FLOP rates.
  • Workload metrics are computed for annotated code regions; empirical data are fused with static estimates.
• Data Manipulation & Visualization:
  • JSON storage schema: hardware_roofs and kernel_runs as top-level arrays; metadata structure for provenance and multi-system comparisons.
  • Data ingestion using Jackson; mapping from JSON to RoofSegment objects defining roofs and data points.
  • Interactive charting (JavaFX/Eclipse): log$_2$-scaled axes, mouseover and context menus, drag-zoom, filtering, export.

These components are orchestrated so that users can launch microbenchmarks, collect kernel performance results, and obtain immediate Roofline-based insights all within a developer's IDE. Integration into Eclipse enables direct navigation between code and performance plots; kernels can be cross-referenced to source via project navigator.

3. Implementation Technology and Data Model

The visualization engine is implemented in Java 8, using JavaFX for charting, and embedded as an Eclipse plugin (via SWT–JavaFX bridge and PDE). Data inputs are JSON and optionally CSV, structured with arrays for:

• hardware_roofs: [{name, bandwidth/flop_rate}, ...]
• kernel_runs: [{kernel, intensity, performance}, ...] Example:

  { "metadata": { "system": "Hopper", "date": "2014-07-01" }, "hardware_roofs": [ { "name": "DRAM", "bandwidth": 51e9 }, { "name": "L2", "bandwidth": 256e9 }, { "name": "PeakFLOP", "flop_rate": 1.05e12 } ], "kernel_runs": [ { "kernel": "dgemm", "intensity": 8.0, "performance": 0.92e12 } ] }

  Axes are log$_2$ scaled using custom tick position algorithms. Each roof segment is specified by its endpoints $(I_{\min}, \beta I_{\min})$ to $(I_{\max}, \beta I_{\max})$; horizontal compute roofs are drawn at $y = P_\text{peak}$. Extensibility is supported via the RoofSegment interface.

4. User Workflow and Visualization

A typical workflow involves:

1. Loading architectural data: "Load System Data..." imports a JSON with measured or specified hardware ceilings.
2. Adding kernel measurements: "Add Kernel Run..." links CSV/JSON results for specific kernel invocations; points are plotted.
3. Interactive exploration: right-click retrieves numeric values, highlights the critical path, and supports direct source-code navigation.
4. Export and comparative analysis: charts can be exported, data sets switched, or multi-system overlays created for rapid cross-hardware evaluation.

Example scenario:

• Three measured roofs: DRAM (51 GB/s), L2 (256 GB/s), PeakFLOP (1.05 TFLOP/s).
• DGEMM kernel measured at $I=8$, $P=0.92$ TF/s; plotted point sits below $P_\text{peak}$, indicating compute-bound but with 13% theoretical headroom.
• DRAM to compute intersection at $I \approx 20$ signals DRAM bottleneck for $I < 20$; as $I$ increases, kernel transitions to compute-bound. Profiling guides developers to focus on L2 optimization as a next step.

5. Extensibility, Multi-Metric, and Future Directions

Ongoing and planned extensions include:

• Multi-system comparison: concurrent display of several models for hardware cross-analysis.
• Enhanced data repositories: k-indexed community JSON roofline databases, searchable via RESTful APIs.
• Automated model generation: PBound-style static analysis and MAQAO-style binary analysis for arithmetic intensity inference.
• Deep IDE integration: launching benchmarks, real-time feedback as code is edited, linkage to TAUdb performance traces.
• New roof types: energy efficiency ("Watts/FLOP"), mixed-precision bounds, accelerator- and on-chip-network-specific metrics. These expansions aim to embed Roofline analysis within the developer workflow, with full coverage from hardware measurement through optimization recommendations.

6. Integration with Performance Toolchains

The Toolkit is designed for compatibility with established HPC performance analysis tools and model generators:

• Empirical bandwidth and compute ceilings are obtained using portable microbenchmarks; for sophisticated hierarchy analysis, adjunct packages such as ERT (Empirical Roofline Toolkit) and platform-specific profilers (TAU, Intel Advisor, Nsight Compute) are leveraged.
• Kernel metrics may be gathered from static or dynamic instrumentation, including extended support for LLVM, binary analysis, and integration with profiling databases.

An extensible plugin architecture facilitates rapid incorporation of new device types, additional roofline metrics, and adaptation to evolving HPC platforms.

7. Significance and Impact

The Roofline Toolkit provides a unified, extensible infrastructure for mapping application bottlenecks onto hardware limits, empowering empirically-driven optimization strategies. Its design reflects the need for reproducible, scalable performance modeling across diverse architectures and workloads. By encapsulating all steps—ceiling measurement, intensity estimation, immediate visualization, and actionable feedback—in a modular environment, it transitions Roofline analysis from specialist methodology to first-class engineering practice (Spear et al., 2015). The approach supports both educational usage for performance modeling instruction and advanced deployment in production performance engineering and architecture co-design.