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Ekow Wellington fd76507e9a Install default plans under MSCCLPP_CACHE_DIR/default (#769 )

### Summary
Update the installer to place bundled default execution plans under
`<MSCCLPP_CACHE_DIR>/default`, which is where the runtime already looks
for bundled plans.

### Background
The C++ runtime treats `MSCCLPP_CACHE_DIR` as the cache *root* and loads
bundled default plans from `<cache root>/default`.
When `MSCCLPP_CACHE_DIR` was set, the installer instead wrote bundled
plans
directly into the cache root, causing the runtime to miss them.

This surfaced while running benchmarking tests with a non-default
`MSCCLPP_CACHE_DIR`, where the bundled plans were not being discovered.

### Change
This PR updates the installer to always install bundled default plans
into
`<MSCCLPP_CACHE_DIR>/default`, preserving the existing runtime contract.

### Scope
- Installer-only change
- No runtime behavior changes

### Validation
Manual inspection of the updated install path.
Successful build

---------

Co-authored-by: Ekow Wellington <t-ekoww@microsoft.com>

2026-03-31 14:27:33 -05:00

2.5 KiB

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Results

This page presents performance benchmarks for collective communication algorithms implemented using the MSCCL++ DSL (Domain Specific Language).

Available Algorithms

The following reference implementations are provided:

Single-Node AllReduce on H100 (NVLS)

We evaluate a single-node AllReduce algorithm designed for NVIDIA H100 GPUs leveraging NVLink Switch (NVLS) technology. This algorithm demonstrates optimal performance for intra-node collective operations.

Source Code Location:

The algorithm implementation can be found at:

mscclpp/python/mscclpp/language/tests

Running the Benchmark:

Users can generate the corresponding JSON execution plan by following the steps described in the Quick Start section. Once the JSON file is generated, it can be executed using the executor_test.py tool to measure performance.

Performance Results:

The following figures show the achieved bandwidth for message sizes ranging from 1KB to 1GB:

:name: single-node-allreduce-small
:alt: Single-node AllReduce performance (1KB to 1MB)
:align: center

Single-node AllReduce performance on H100 with NVLS (1KB to 1MB message sizes)

:name: single-node-allreduce-large
:alt: Single-node AllReduce performance (1MB to 1GB)
:align: center

Single-node AllReduce performance on H100 with NVLS (1MB to 1GB message sizes)

Two-Node AllReduce on H100 (Small Message Sizes)

We also provide a two-node AllReduce algorithm for H100 GPUs, specifically optimized for small message sizes. This algorithm uses a non-zero-copy communication path to minimize latency for small data transfers.

Installation:

This algorithm is installed by default when running:

python3 -m mscclpp --install

Execution Plan Location:

After installation, the generated JSON execution plan can be found at:

~/.cache/mscclpp/default/

If MSCCLPP_CACHE_DIR is set, bundled default plans are installed under MSCCLPP_CACHE_DIR/default/. MSCCLPP_CACHE_DIR specifies the cache root directory, so it should be set without default in the path.