feat: Add mock servers for DGX H100, H200, and B200 systems by fabiendupont · Pull Request #163 · NVIDIA/go-nvml

fabiendupont · 2025-09-09T14:40:05Z

This commit adds comprehensive mock implementations for three additional NVIDIA GPU architectures to support multi-architecture testing:

dgxh100: 8-GPU DGX H100 system with Hopper architecture
dgxh200: 8-GPU DGX H200 system with Hopper architecture
dgxb200: 8-GPU DGX B200 system with Blackwell architecture

Each mock server provides:

Realistic device properties (memory, compute capability, architecture)
Complete MIG profile support with architecture-specific configurations
Full NVML interface implementation following existing dgxa100 patterns
Thread-safe operations with proper locking
Comprehensive MIG placement and profile information

Key Features:

H100/H200: 141GB memory, Hopper architecture, enhanced MIG profiles
B200: 192GB memory, Blackwell architecture, current-gen capabilities
Consistent API with existing dgxa100 mock server
Proper error handling and return codes
Realistic PCI bus IDs and device UUIDs

This enables comprehensive testing across multiple GPU architectures without requiring physical hardware.

Copilot

Pull Request Overview

This PR adds comprehensive mock implementations for three additional NVIDIA GPU architectures (H100, H200, and B200) to support multi-architecture testing without requiring physical hardware.

Implements complete mock servers for DGX H100, H200, and B200 systems with 8-GPU configurations
Provides architecture-specific MIG profiles with realistic memory allocations and compute capabilities
Adds fabric management integration and enhanced topology features for newer GPU architectures

Reviewed Changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated no comments.

Show a summary per file

File	Description
pkg/nvml/mock/dgxh100/dgxh100.go	Main mock server implementation for H100 with Hopper architecture (80GB memory, 9.0 compute capability)
pkg/nvml/mock/dgxh100/mig-profile.go	H100-specific MIG profiles with enhanced memory allocations compared to A100
pkg/nvml/mock/dgxh200/dgxh200.go	H200 mock server with enhanced memory (141GB) and improved fabric capabilities
pkg/nvml/mock/dgxh200/mig-profile.go	H200 MIG profiles with 76% more memory than H100 profiles
pkg/nvml/mock/dgxb200/dgxb200.go	B200 mock server with Blackwell architecture (192GB memory, 10.0 compute capability)
pkg/nvml/mock/dgxb200/mig-profile.go	B200 MIG profiles with massive memory allocations for next-generation AI workloads

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ArangoGutierrez

First pass looks good, I'll run some manual tests and provide feedback

elezar · 2025-09-10T07:25:50Z

+	return device
+}
+
+func (d *Device) GetUUID() (string, nvml.Return) {


Why do we not set the mock functions as we do for the a100?

Agree @fabiendupont let's do as https://github.com/NVIDIA/go-nvml/blob/main/pkg/nvml/mock/dgxa100/dgxa100.go#L195 and https://github.com/NVIDIA/go-nvml/blob/main/pkg/nvml/mock/dgxa100/dgxa100.go#L136 for the 3 B200,H100 and H200

elezar · 2025-09-10T07:28:04Z

+	CudaDriverVersion int
+}
+
+type Device struct {


Question: Why do we need a new type. Are the properties not expected to be the same across different devices?

I think @fabiendupont intend was to create a folder for each GPU type (given that current A100 implementation is a folder named a100) If we were to add more GPU types, then we should rename that folder to a generic name, and that way we don't need to duplicate this much code, simply a file per GPU type and MIG profile. reusing a core set of defined variables

Copilot

Pull Request Overview

Copilot reviewed 6 out of 6 changed files in this pull request and generated 3 comments.

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Copilot · 2025-09-17T12:19:32Z

+		MigMode:              nvml.DEVICE_MIG_ENABLE,
+		GpuInstances:         make(map[*GpuInstance]struct{}),
+		GpuInstanceCounter:   0,
+		MemoryInfo:           nvml.Memory{Total: 151397597184}, // 141GB for H200 (vs 80GB for H100)


The memory calculation is incorrect. 151397597184 bytes equals approximately 141GB, but this should be calculated as 141 * 1024^3 = 151473709056 bytes for exact 141GB.

Suggested change

MemoryInfo: nvml.Memory{Total: 151397597184}, // 141GB for H200 (vs 80GB for H100)

MemoryInfo: nvml.Memory{Total: 141 * 1024 * 1024 * 1024}, // 141GB for H200 (vs 80GB for H100)

Copilot · 2025-09-17T12:19:33Z

+		MigMode:              nvml.DEVICE_MIG_ENABLE,
+		GpuInstances:         make(map[*GpuInstance]struct{}),
+		GpuInstanceCounter:   0,
+		MemoryInfo:           nvml.Memory{Total: 206158430208}, // 192GB for B200 (massive memory)


The memory calculation is incorrect. 206158430208 bytes equals approximately 192GB, but this should be calculated as 192 * 1024^3 = 206158430208 bytes. While the value is correct, it should be calculated consistently.

Suggested change

MemoryInfo: nvml.Memory{Total: 206158430208}, // 192GB for B200 (massive memory)

MemoryInfo: nvml.Memory{Total: 192 * 1024 * 1024 * 1024}, // 192GB for B200 (massive memory)

Copilot · 2025-09-17T12:19:33Z

+		MigMode:              nvml.DEVICE_MIG_ENABLE,
+		GpuInstances:         make(map[*GpuInstance]struct{}),
+		GpuInstanceCounter:   0,
+		MemoryInfo:           nvml.Memory{Total: 85899345920}, // 80GB for H100


The memory calculation is incorrect. 85899345920 bytes equals approximately 80GB, but this should be calculated as 80 * 1024^3 = 85899345920 bytes for exact 80GB.

Suggested change

MemoryInfo: nvml.Memory{Total: 85899345920}, // 80GB for H100

MemoryInfo: nvml.Memory{Total: 80 * 1024 * 1024 * 1024}, // 80GB for H100

fabiendupont · 2025-09-29T13:04:27Z

Added mock tests, to have basic regression testsing.
Refactored the mocks by decoupling GPU/Server logic from configuration.
Added A30 as an example on Ampere generation for the refactor.
Added H100, H200 and B200, alongside with documentation of the refactor.

Copilot

Pull Request Overview

Copilot reviewed 16 out of 16 changed files in this pull request and generated 1 comment.

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Copilot · 2025-10-07T09:51:20Z

+	},
+	nvml.GPU_INSTANCE_PROFILE_1_SLICE_REV1: {
+		nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE: {
+			Id:                  nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE_REV1,


The compute instance profile ID should be nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE not nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE_REV1. The REV1 suffix applies to GPU instance profiles, not compute instance profiles.

Suggested change

Id: nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE_REV1,

Id: nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE,

ArangoGutierrez

PR looks now now
@elezar - @klueska Ready for your approval

elezar

Some quick thoughts on this. I will make a more critical pass later this week.

elezar · 2025-10-20T11:17:59Z

+	require.Implements(t, (*nvml.Interface)(nil), server)
+	require.Implements(t, (*nvml.ExtendedInterface)(nil), server)


We can test these at compile time with something like:

var _ nvml.Interface = (*server)(nil) var _ nvml.ExtendedInterface = (*server)(nil)

Added tests

elezar · 2025-10-20T11:23:19Z

+
+Add new configurations to the appropriate file in `shared/gpus/`:
+```go
+var A100_PCIE_24GB = shared.Config{


nit: Do we want a more go-like name?

elezar · 2025-10-20T11:24:26Z

+
+```
+pkg/nvml/mock/
+├── shared/


Should shared be internal? Do we expect that users interact with this package?

Moved it to internal. Users still have an external API through package-specific wrappers.

elezar · 2025-10-20T11:34:57Z

+		PciDeviceId:        config.PciDeviceId,
+		MIGProfiles:        config.MIGProfiles,
+	}
+	device.SetMockFuncs()


Note: Since a device is only functional after setting these mocks, should we be exposing it as a public concrete type?

elezar · 2025-10-20T11:36:44Z

+type Server = shared.Server
+type Device = shared.Device
+type GpuInstance = shared.GpuInstance
+type ComputeInstance = shared.ComputeInstance
+type CudaComputeCapability = shared.CudaComputeCapability


Since this is a new package, we probably don't need backward compatible types.

elezar · 2025-10-20T11:37:36Z

+func New() *Server {
+	return shared.NewServerFromConfig(shared.ServerConfig{
+		Config:            gpus.H100_SXM5_80GB,
+		GPUCount:          8,


Question: Instead of specifying the count, why not pass in a list of devices? This will also make it simpler to handle heterogenous configs.

Great point. Modified to passing a list and added test for heterogeneous GPUs.

elezar

Sorry for the delay here. I am revisiting this due to my wanting to add a specific mock for something that I'm working on.

Looking at the orginisation, I think the SPECIFIC GPU implementations are useful -- as is the scaffolding to create a server with a specific list of GPUs or a COUNG of a single GPU type.

In my case, I want to mock a Tegra-based system which includes an iGPU and a dGPU. Here I would imaging instantiating this as:

NewServer(WithGPUs(gpus.IGX_THOR, gpus.RTX_PRO_6000))

Let's assume that I don't care about the driver version and / or the CUDA version in this case.

WIth this in mind, I have some proposed changes under https://github.com/elezar/go-nvml/tree/feat/add-dgxh100-dgxh200-dgxb200-mocks

I also have a branch: https://github.com/elezar/go-nvml/tree/add-tegra-mocks that adds a tegra (Thor) device.

Copilot

Pull request overview

Copilot reviewed 16 out of 16 changed files in this pull request and generated 13 comments.

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+	}
+
+	gi.GetComputeInstancePossiblePlacementsFunc = func(info *nvml.ComputeInstanceProfileInfo) ([]nvml.ComputeInstancePlacement, nvml.Return) {
+		return gi.MIGProfiles.ComputeInstancePlacements[int(gi.Info.Id)][int(info.Id)], nvml.SUCCESS


+		d.MigMode = mode
+		return nvml.SUCCESS, nvml.SUCCESS
+	}
+
+	d.GetMigModeFunc = func() (int, int, nvml.Return) {
+		return d.MigMode, d.MigMode, nvml.SUCCESS


+	nvml.GPU_INSTANCE_PROFILE_1_SLICE_REV1: {
+		nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE: {
+			Id:                  nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE_REV1,
+			SliceCount:          1,
+			InstanceCount:       1,
+			MultiprocessorCount: 14,
+		},


+var a100_ComputeInstanceProfiles = map[int]map[int]nvml.ComputeInstanceProfileInfo{
+	nvml.GPU_INSTANCE_PROFILE_1_SLICE: {
+		nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE: {
+			Id:                  nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE,
+			SliceCount:          1,
+			InstanceCount:       1,
+			MultiprocessorCount: 16,
+		},
+	},
+	nvml.GPU_INSTANCE_PROFILE_2_SLICE: {
+		nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE: {
+			Id:                  nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE,
+			SliceCount:          1,
+			InstanceCount:       2,
+			MultiprocessorCount: 16,
+		},
+		nvml.COMPUTE_INSTANCE_PROFILE_2_SLICE: {
+			Id:                  nvml.COMPUTE_INSTANCE_PROFILE_2_SLICE,


+var a100_GpuInstancePlacements = map[int][]nvml.GpuInstancePlacement{
+	nvml.GPU_INSTANCE_PROFILE_1_SLICE: {
+		{Start: 0, Size: 1},
+		{Start: 1, Size: 1},
+		{Start: 2, Size: 1},
+		{Start: 3, Size: 1},
+		{Start: 4, Size: 1},
+		{Start: 5, Size: 1},
+		{Start: 6, Size: 1},
+	},
+	nvml.GPU_INSTANCE_PROFILE_2_SLICE: {
+		{Start: 0, Size: 2},
+		{Start: 2, Size: 2},
+		{Start: 4, Size: 2},
+	},
+	nvml.GPU_INSTANCE_PROFILE_3_SLICE: {
+		{Start: 0, Size: 3},
+		{Start: 4, Size: 3},
+	},
+	nvml.GPU_INSTANCE_PROFILE_4_SLICE: {
+		{Start: 0, Size: 4},
+	},
+	nvml.GPU_INSTANCE_PROFILE_7_SLICE: {
+		{Start: 0, Size: 8}, // Test expects Size 8
+	},
+}


+	},
+	nvml.GPU_INSTANCE_PROFILE_2_SLICE: {
+		{Start: 0, Size: 2},
+		{Start: 2, Size: 2},
+		{Start: 4, Size: 2},
+	},
+	nvml.GPU_INSTANCE_PROFILE_3_SLICE: {
+		{Start: 0, Size: 3},
+		{Start: 4, Size: 3},
+	},
+	nvml.GPU_INSTANCE_PROFILE_4_SLICE: {
+		{Start: 0, Size: 4},
+	},
+	nvml.GPU_INSTANCE_PROFILE_7_SLICE: {
+		{Start: 0, Size: 7},
+	},


+```go
+import (
+    "github.com/NVIDIA/go-nvml/pkg/nvml/mock/dgxa100"
+    "github.com/NVIDIA/go-nvml/pkg/nvml/mock/dgxh100"
+    "github.com/NVIDIA/go-nvml/pkg/nvml/mock/dgxh200"
+    "github.com/NVIDIA/go-nvml/pkg/nvml/mock/dgxb200"
+    "github.com/NVIDIA/go-nvml/pkg/nvml/mock/internal/shared/gpus"
+)
+


+
+- All existing `dgxa100.New()`, `dgxh100.New()`, `dgxh200.New()`, `dgxb200.New()` calls continue to work unchanged
+- Legacy global variables (`MIGProfiles`, `MIGPlacements`) are preserved for all generations
+- Device names maintain "Mock" prefix for test compatibility


+├── shared/
+│   ├── shared.go                 # Core shared factory and types
+│   └── gpus/                     # GPU configuration definitions
+│       ├── a100.go              # A100 GPU variants (Ampere)
+│       ├── a30.go               # A30 GPU variants (Ampere)
+│       ├── h100.go              # H100 GPU variants (Hopper)
+│       ├── h200.go              # H200 GPU variants (Hopper)
+│       └── b200.go              # B200 GPU variants (Blackwell)


+var a100_ComputeInstanceProfiles = map[int]map[int]nvml.ComputeInstanceProfileInfo{
+	nvml.GPU_INSTANCE_PROFILE_1_SLICE: {
+		nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE: {
+			Id:                  nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE,
+			SliceCount:          1,
+			InstanceCount:       1,
+			MultiprocessorCount: 16,
+		},
+	},
+	nvml.GPU_INSTANCE_PROFILE_2_SLICE: {


Establish baseline tests for the original A100 implementation before refactoring to shared architecture. This enables proper TDD approach where we can detect regressions during refactoring. Tests cover: - Server creation and basic properties - Device handling and indexing (8 devices) - Device properties (name, architecture, memory, etc.) - Device access by UUID and PCI bus ID - MIG mode operations - MIG profile configurations and access - GPU instance lifecycle and placements - Compute instance lifecycle - Init/shutdown behavior - Multiple device uniqueness - A100-specific characteristics All tests pass with the existing implementation at fd3e42f. Signed-off-by: Fabien Dupont <fdupont@redhat.com>

- Implement shared factory system in pkg/nvml/mock/shared/ to eliminate code duplication - Add comprehensive A30 GPU configurations with MIG profiles (56 SMs, 1/2/4-slice support) - Refactor dgxa100 to use shared factory while maintaining backward compatibility - Create modular GPU configurations in shared/gpus/ for A100 and A30 families - Add comprehensive documentation covering architecture and usage examples - Maintain thread safety and proper NVML return codes - Support all A100 variants (SXM4 40GB/80GB, PCIe 40GB/80GB) and A30 PCIe 24GB Signed-off-by: Fabien Dupont <fdupont@redhat.com>

Implements DGX H100 and H200 GPU mocks following the established shared factory pattern for consistency with existing A100/A30 implementations. - Add H100 SXM5 80GB configuration with complete MIG profile support - Add H200 SXM5 141GB configuration with complete MIG profile support - Implement dgxh100 and dgxh200 packages using shared factory pattern - Include all 7 MIG profiles (standard, REV1 media, REV2 double memory) - Maintain full backward compatibility with legacy globals and type aliases - Use NVIDIA-spec compliant memory allocations and SM distributions Signed-off-by: Fabien Dupont <fdupont@redhat.com>

Implements DGX B200 mock following the established shared factory pattern: - Add B200 SXM5 180GB GPU configuration with Blackwell architecture - Comprehensive MIG profiles matching NVIDIA specifications: * Memory allocations: 23GB, 45GB, 90GB, 180GB per NVIDIA MIG User Guide * REV1 (media extensions) and REV2 (expanded memory) profiles * Full P2P support in MIG mode (IsP2pSupported: 1) * 144 SMs total with 18 SMs per slice - Complete DGX B200 server implementation with 8 GPUs - Driver version 560.28.03, NVML 12.560.28.03, CUDA 12060 - Comprehensive test suite covering server, device, and MIG operations - Backward compatible legacy global variables (MIGProfiles, MIGPlacements) Memory values corrected from initial 192GB to 180GB based on official NVIDIA MIG User Guide specifications. Signed-off-by: Fabien Dupont <fdupont@redhat.com>

…ntation Updates the mock framework documentation to include all GPU generations: - Add H100, H200, and B200 to architecture diagram and file structure - Document all GPU specifications: * H100 SXM5 80GB (Hopper, 132 SMs, CUDA 9.0, P2P MIG support) * H200 SXM5 141GB (Hopper, 132 SMs, CUDA 9.0, P2P MIG support) * B200 SXM5 180GB (Blackwell, 144 SMs, CUDA 10.0, P2P MIG support) - Add complete server model documentation with driver versions - Expand MIG support section with detailed SM allocations and P2P capabilities - Update usage examples to include all four GPU generations - Add comprehensive testing instructions for all mock implementations - Update backward compatibility section to reflect all generations The documentation now accurately reflects the complete shared factory implementation with comprehensive coverage of Ampere, Hopper, and Blackwell GPU architectures. Signed-off-by: Fabien Dupont <fdupont@redhat.com>

@elezar

This commit addresses feedback from PR review #3356107365 by @elezar, implementing architectural improvements and code quality enhancements. - Added compile-time interface assertions to all test files - `var _ nvml.Interface = (*shared.Server)(nil)` pattern - Catches interface compliance issues at compile time vs runtime - Added missing trailing newline to dgxa100_test.go - Removed unnecessary type aliases from new packages (dgxh100, dgxh200, dgxb200) - New packages now use shared.Server directly instead of local aliases - Moved pkg/nvml/mock/shared → pkg/nvml/mock/internal/shared - Hides implementation details from external users - Public API remains unchanged through package-specific wrappers - Reduces field duplication (Name, Brand, Architecture, etc.) - Clearer relationship between configuration and device - Fields now accessed via d.Config.Name, d.Config.Brand, etc. - Removed 5+ duplicate fields from Device struct - Changed `Devices [8]nvml.Device` → `Devices []nvml.Device` - Removes hardcoded 8-device limit - Enables flexible GPU counts and heterogeneous configurations - `const GBtoMB = 1024` for clear memory calculations - Improves readability: `24 * GBtoMB` vs `24576` - Implemented `NewServerWithGPUs(...Config)` with variadic parameters - Supports mixed GPU types in single server - Example: `NewServerWithGPUs(gpus.A100_SXM4_40GB, gpus.A100_SXM4_80GB)` - Added comprehensive test: TestHeterogeneousGPUs - Added deprecation comments to backward-compatible type aliases - Deprecated single-GPU functions in favor of flexible alternatives - Maintains backward compatibility while guiding users to better APIs - All existing tests pass (dgxa100, dgxh100, dgxh200, dgxb200) - Added TestHeterogeneousGPUs demonstrating mixed GPU configurations - No breaking changes to public API Signed-off-by: Fabien Dupont <fdupont@redhat.com>

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ArangoGutierrez mentioned this pull request Oct 8, 2025

Add GPU Mock Infrastructure NVIDIA/k8s-test-infra#180

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fabiendupont added 4 commits March 23, 2026 15:33

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	MemoryInfo: nvml.Memory{Total: 151397597184}, // 141GB for H200 (vs 80GB for H100)
	MemoryInfo: nvml.Memory{Total: 141 * 1024 * 1024 * 1024}, // 141GB for H200 (vs 80GB for H100)

	MemoryInfo: nvml.Memory{Total: 206158430208}, // 192GB for B200 (massive memory)
	MemoryInfo: nvml.Memory{Total: 192 * 1024 * 1024 * 1024}, // 192GB for B200 (massive memory)

	MemoryInfo: nvml.Memory{Total: 85899345920}, // 80GB for H100
	MemoryInfo: nvml.Memory{Total: 80 * 1024 * 1024 * 1024}, // 80GB for H100

	Id: nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE_REV1,
	Id: nvml.COMPUTE_INSTANCE_PROFILE_1_SLICE,

		require.Implements(t, (*nvml.Interface)(nil), server)
		require.Implements(t, (*nvml.ExtendedInterface)(nil), server)

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fabiendupont Oct 24, 2025 •

edited

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