E4S Performance Tools Guide
Construct your prompt from the instructions below then use the E4S Guide Bot
Introduction
Selecting a suitable performance tool from the E4S suite depends on understanding your target system, application characteristics, and desired insights. The E4S 25.06 release includes a range of profiling, tracing, analysis, and performance modeling tools that cover CPU, GPU, memory, network, and I/O performance.
Performance tools can operate at different scales (intra-node vs inter-node), use various data collection methods (sampling, tracing, instrumentation), and integrate with compilers or runtime systems. Choosing the right tool requires balancing the level of detail, overhead, portability, and ease of use.
The following attributes help guide this selection process by describing characteristics of the application, the system environment, and the type of performance data desired.
Example Prompt
I am running a large CFD simulation on a multi-node GPU cluster with both NVIDIA and AMD devices. I want to identify memory bottlenecks and GPU kernel inefficiencies without significantly impacting runtime. I prefer a tool that integrates with MPI and provides both timeline and statistical reports, with visualization support through Paraver or similar tools.
Broadly Meaningful Attributes for Performance Tools
| Attribute | Description |
|---|---|
| Programming Model | The programming models used (MPI, OpenMP, CUDA, HIP, SYCL, etc.) that the tool must support |
| Hardware Type | Target architecture such as CPUs, GPUs, accelerators, or hybrid systems |
| Instrumentation Method | The way performance data is collected (sampling, tracing, instrumentation) |
| Profiling Scope | Whether the tool analyzes a single process, a node, or the full distributed job |
| Data Visualization | Availability of graphical or command-line interfaces for performance analysis |
| Overhead Sensitivity | The degree to which the tool impacts application performance during measurement |
| Integration | Compatibility with compilers, debuggers, or workflow tools in the E4S ecosystem |
| Output Format | Supported output data types such as JSON, XML, SQLite, or custom formats |
| Automation Features | Support for automated analysis, pattern detection, or performance tuning suggestions |
| License Type | Open source or vendor-provided license requirements |
| Platform Portability | Availability on Linux, macOS, Windows, or HPC operating environments |
| Ease of Use | Level of expertise required to install, run, and interpret results |
Attributes for Specific Situations
Intra-Node Tools
| Attribute | Description |
|---|---|
| Thread-Level Detail | Ability to profile per-thread performance (e.g., OpenMP regions, CPU cores) |
| GPU Kernel Analysis | Support for analyzing GPU kernel execution times, occupancy, and memory throughput |
| Memory Hierarchy Profiling | Ability to measure cache misses, memory bandwidth, and NUMA locality |
| Sampling Granularity | Level of temporal detail available for short or long running kernels |
| Compiler Integration | Ability to automatically instrument or analyze code through compiler interfaces |
| Source-Level Correlation | Mapping of collected data back to source code lines or functions |
Inter-Node Tools
| Attribute | Description |
|---|---|
| MPI Tracing | Ability to record message-passing events and communication timelines |
| Collective Operation Analysis | Evaluation of collective operation costs and imbalance |
| Load Imbalance Detection | Identification of synchronization or idle-time bottlenecks |
| Scalability Testing | Ability to handle large-scale job sizes efficiently |
| Network Profiling | Collection of data about network latency, bandwidth, and contention |
| Parallel File I/O | Instrumentation of parallel file reads/writes through MPI-IO or HDF5 |
Vendor-Provided Tools (NVIDIA, AMD, Intel, ARM)
| Attribute | Description |
|---|---|
| Vendor SDK Integration | Compatibility with vendor development kits such as CUDA Toolkit, ROCm, or oneAPI |
| Hardware Counter Access | Access to low-level hardware counters specific to the vendor architecture |
| GPU-CPU Correlation | Cross-analysis of GPU and CPU events in the same timeline |
| Driver-Level Analysis | Ability to inspect driver or kernel-level events for performance tuning |
| IDE or GUI Availability | Whether the vendor tool includes a visual interface for analysis |
| Multi-Vendor Support | Whether the tool supports analysis across different vendor devices in the same job |