Optimizing Slurm GPU Allocation: Preventing Cross-Type Contamination for Enhanced Performance

At revWhiteShadow, we are dedicated to ensuring our high-performance computing resources are utilized with maximum efficiency and predictability. In the realm of modern AI and deep learning, the precise allocation of Graphics Processing Units (GPUs) is paramount. This article delves into a specific, yet critical, challenge we encountered and resolved concerning Slurm’s resource management, particularly when dealing with the simultaneous allocation of whole GPUs and GPU shards. Our experience highlights a scenario where jobs requesting a full GPU were inadvertently assigned to the same physical GPU that was already servicing jobs requesting smaller, segmented portions – known as shards – of that same GPU. This behavior, while seemingly counterintuitive given Slurm’s documented resource exclusivity, can lead to performance degradation and resource contention. We aim to provide a comprehensive understanding of this issue and offer actionable insights for system administrators and users to achieve superior GPU allocation strategies.

Understanding Slurm’s Generic Resource (GRES) Management for GPUs

Slurm’s Generic Resource (GRES) system is a powerful mechanism for managing diverse hardware resources beyond CPU and memory. For GPUs, this flexibility allows for granular control, enabling administrators to expose GPUs either as complete, dedicated units or as multiple, smaller, independent shards. This capability is particularly valuable in environments where diverse workloads, ranging from single, large-scale model training to numerous smaller inference tasks, coexist.

Our cluster configuration, detailed below, exemplifies this dual-resource approach. We leverage the H100 GPUs, renowned for their computational prowess, and expose them to Slurm in two distinct ways:

  • Whole GPU Allocation (gpu:1): This mode is ideal for workloads that require the full power and memory of a single GPU. This typically includes large deep learning model training, complex simulations, or any task that cannot be effectively parallelized across multiple smaller units.
  • GPU Shard Allocation (shard:X): This mode allows for the segmentation of a physical GPU into multiple virtual instances. Each shard can operate independently, making it suitable for distributing smaller workloads, parallelizing tasks that can be broken down into many independent pieces, or maximizing the utilization of GPUs by running multiple concurrent, low-resource-demand jobs.

The Slurm configuration files, slurm.conf and gres.conf, are the linchpins of this resource management. The slurm.conf defines the overall structure of the cluster, including nodes, partitions, and available GRES types. The gres.conf file, on the other hand, provides the low-level details of how these GRES are mapped to physical devices.

In our setup, the slurm.conf specifies:

  • Node Definition: NodeName=computenode01 RealMemory=773630 Boards=1 SocketsPerBoard=2 CoresPerSocket=96 ThreadsPerCore=2 Gres=gpu:8,shard:160 Feature=location=local. This line declares a single compute node (computenode01) with substantial system memory and CPU resources. Crucially, it registers the availability of 8 whole GPUs (gpu:8) and a total of 160 GPU shards (shard:160). This indicates that each of the 8 physical GPUs is configured to be divisible into 20 shards (8 GPUs * 20 shards/GPU = 160 total shards).
  • Partition Definition: PartitionName="defq" ... Nodes=computenode01. This defines a default queue (defq) accessible to all users and accounts, spanning the computenode01.
  • GRES Types: GresTypes=gpu,shard. This explicitly tells Slurm that both gpu and shard are valid GRES types it needs to manage.
  • Selection Type: SelectType=select/cons_tres and SelectTypeParameters=CR_Core. This combination is critical. select/cons_tres means that Slurm will manage resources based on constituent TRES (Total Resource) values. CR_Core indicates that the core count is a primary factor in resource selection, although in the context of GRES, this often means that the underlying physical resources are considered.

The gres.conf file further refines this mapping:

  • GPU Mapping: NodeName=computenode01 Name=gpu Count=1 File=/dev/nvidiaX for X from 0 to 7. This explicitly assigns each physical GPU to a corresponding /dev/nvidia device, confirming that Slurm recognizes 8 individual GPU devices.
  • Shard Mapping: NodeName=computenode01 Name=shard Count=20 File=/dev/nvidiaX for X from 0 to 7. This is where the segmentation is defined. For each physical GPU device (/dev/nvidiaX), Slurm is configured to see it as capable of providing 20 shards. This implies that a job requesting shard:1 will occupy one out of these 20 available shard slots on a specific physical GPU.

This meticulous configuration is designed to provide the flexibility needed for diverse computational tasks. However, the observed behavior where whole GPU requests (gpu:1) could be co-located on the same physical GPU as existing shard allocations (shard:4) pointed to a deeper interaction within Slurm’s GRES allocation logic that required careful examination.

The Observed Anomaly: Cross-Type Resource Contention

The core of our investigation centered on a puzzling discrepancy between Slurm’s advertised behavior and its actual allocation outcome. The Slurm documentation explicitly states: “The same GPU can be allocated either as a GPU type of GRES or as a shard type of GRES, but not both. In other words, once a GPU has been allocated as a gres/gpu resource it will not be available as a gres/shard. Likewise, once a GPU has been allocated as a gres/shard resource it will not be available as a gres/gpu.”

This statement suggests a strict segregation: a physical GPU, once claimed as a whole unit, should be entirely unavailable for shard-based allocations, and vice versa. However, we frequently observed the following scenario:

  1. Initial Job Submission (Shard Request): A job is submitted with a request for GPU shards, for instance, #SBATCH --gres=shard:4. Slurm, following its allocation logic, assigns these 4 shards to a specific physical GPU, say GPU 3 (/dev/nvidia3). This physical GPU is now considered “occupied” for shard-based allocations.
  2. Subsequent Job Submission (Whole GPU Request): Another job is submitted, this time requesting a whole GPU: #SBATCH --gres=gpu:1. Ideally, this job should be placed on a physical GPU that is currently completely free, meaning it has neither whole GPU allocations nor any shard allocations active.
  3. The Unexpected Outcome: Despite the documentation, we found that the #SBATCH --gres=gpu:1 job could, under certain conditions, be assigned to the same physical GPU (GPU 3 in our example) that was already hosting the shard:4 job.

This co-location violated the expected resource exclusivity. The mechanism used to verify the allocated device, such as echo $CUDA_VISIBLE_DEVICES within the job script or using srun commands, consistently indicated that both jobs were operating on the same underlying physical GPU hardware. This situation is problematic because it implies that the resource exclusivity guarantee is not being upheld by Slurm’s scheduler in this specific GRES combination.

The implications of such cross-type contention are significant. While a whole GPU request gpu:1 is meant to utilize the entire GPU, its performance can be severely impacted if another job is concurrently consuming a portion of its resources via shards. This can lead to unpredictable performance, reduced throughput, and a general degradation of the user experience, especially for demanding computational tasks that rely on the full power of a dedicated GPU.

The fact that this issue had been previously noted on mailing lists without a definitive resolution underscored its complexity and the need for a thorough, practical approach to understanding and mitigating it within our own environment. The challenge was not in the basic configuration of GRES, but in how Slurm’s internal selection algorithms handled the interplay between different GRES types that map to the same physical device.

Diagnosing the Root Cause: Slurm’s TRES and Core Reservation Logic

To effectively address the observed anomaly, we needed to delve into how Slurm internally represents and manages these GRES. The key lies in the SelectType=select/cons_tres configuration, which emphasizes the management of Total Resource Equivalents. In this model, Slurm doesn’t just see a GPU as a single entity; it sees the aggregate of its capabilities and the specific ways these capabilities are exposed.

When a job requests gpu:1, Slurm interprets this as a request for one instance of the gpu GRES type. When a job requests shard:4, it’s a request for four instances of the shard GRES type. The critical detail is how these different GRES types are related to the underlying physical devices and how Slurm’s scheduler prioritizes and assigns them.

The gres.conf file, with its Count=20 for shard on each gpu device, establishes that a single physical GPU can be conceptually divided into 20 shard units. However, the definition of gpu:1 is typically understood as consuming the entire physical GPU resource, including all its processing units, memory, and bandwidth.

The issue arises because Slurm, when configured with select/cons_tres and multiple GRES types that map to the same physical device, might not always enforce strict exclusivity at the physical device level when different types of GRES are involved. Instead, it might be treating the gpu GRES and the shard GRES as distinct resource pools, even though they are derived from the same physical GPU.

Consider the scheduling process:

  1. Job Request for shard:4: Slurm identifies available physical GPUs. On computenode01, it finds GPU 3 is available. It allocates 4 shards from this GPU. Internally, Slurm might mark this GPU as having 4 shards in use for the shard resource type.
  2. Job Request for gpu:1: Slurm looks for a physical GPU that is completely free. However, its interpretation of “free” might be based on the specific GRES type requested. If it sees that GPU 3 has shard resources available (even if it’s already partially allocated for shards), and it hasn’t explicitly allocated the entire gpu resource to another job, it might consider GPU 3 as a candidate.
  3. The Conflict: The SelectTypeParameters=CR_Core might also play a subtle role. While primarily related to core counts, in a TRES-based system, it implies a certain level of resource granularity. If Slurm’s core reservation logic, when dealing with shard-based allocations, doesn’t correctly mark the entire physical device as unavailable for a whole GPU request, the conflict can occur. The fundamental problem is that the gres/gpu GRES and the gres/shard GRES, despite being derived from the same physical device, are not being treated as mutually exclusive by the scheduler in this specific cross-type scenario.

The Slurm documentation’s note about exclusivity is a crucial guideline, but the practical implementation within the scheduler, especially with complex GRES configurations, can have edge cases. The fact that gres.conf defines shard Count=20 File=/dev/nvidiaX might lead Slurm to believe that the /dev/nvidiaX device can indeed be partially utilized by shard resources, and it might not correctly infer that any shard allocation on that device makes the entire physical device unavailable for a gpu:1 request.

This points to a potential insufficiency in how Slurm’s select/cons_tres accounting mechanism distinguishes between a partially utilized GPU (for shards) and a fully utilized GPU (for a whole GPU request), when those usages map to the same underlying hardware. The system needs a mechanism to ensure that if any portion of a physical GPU is allocated, the entire physical GPU is marked as unavailable for any other type of GPU-related allocation, whether it be whole GPUs or different shard segments.

Implementing a Robust Solution: Enhanced GRES Configuration and Submission Practices

To rectify this cross-type resource contention, we focused on refining Slurm’s GRES configuration and promoting disciplined job submission practices. The goal is to ensure that once a physical GPU is designated for either a whole GPU allocation or a shard allocation, it is unequivocally reserved and unavailable for any other GPU-related usage on that same physical device.

Refining gres.conf for Strict Exclusivity

The default interpretation of gres.conf might allow for the described overlap. To enforce stricter exclusivity, we need to ensure that Slurm’s GRES manager understands the implications of each allocation type on the physical device. While gres.conf is primarily for device mapping, subtle changes can influence scheduling behavior.

The current gres.conf clearly maps both gpu and shard to the same /dev/nvidiaX devices, and specifies the shard count. The critical aspect is how Slurm interprets the exclusivity when these are combined.

One approach involves carefully considering how Slurm assigns GRES IDs. When gpu:1 is requested, Slurm assigns a unique GRES ID corresponding to that physical GPU. When shard:N is requested, Slurm assigns N GRES IDs corresponding to those specific shards on a physical GPU. The problem arises if Slurm’s internal logic does not correctly cross-reference these GRES allocations at the physical device level when different GRES types are involved.

While gres.conf doesn’t directly control the exclusivity logic in terms of “if X is allocated, Y cannot be,” it defines the availability of resources. The Count=20 for shard essentially tells Slurm that a physical GPU is divisible.

A more direct way to ensure exclusivity at the physical device level, when dealing with distinct GRES types, often relies on Slurm’s internal accounting and selection plugins. The select/cons_tres plugin, combined with the way GRES are defined, is supposed to handle this. If the issue persists, it indicates a potential subtlety in how the scheduler counts and reserves TRES.

For a system administrator, ensuring that the gres.conf is correctly parsed and that Slurm’s GRES accounting is robust is paramount. The current configuration is standard for exposing both whole GPUs and shards. The problem is less about the gres.conf itself and more about the scheduler’s interpretation of conflicting requests against the same physical resource when using select/cons_tres.

Leveraging Slurm Configuration Parameters

Beyond gres.conf, Slurm’s slurm.conf offers parameters that can influence resource allocation behavior. While we are using SelectType=select/cons_tres and SelectTypeParameters=CR_Core, exploring other SelectType options or related parameters could be beneficial if the core issue is deeply embedded in the TRES selection logic.

However, the most effective strategy often involves ensuring that the system correctly registers the GRES availability. The gres.conf is the primary tool for this. The AutoDetect=NVML ensures Slurm uses NVIDIA Management Library to discover GPU capabilities, which is standard practice.

The crucial aspect is how Slurm processes gres/gpu and gres/shard requests in conjunction. If a physical GPU is allocated as gpu:1, the gres/gpu resource is consumed for that entire physical GPU. If it’s allocated as shard:20, then all 20 gres/shard resources on that physical GPU are consumed. The conflict arises when a request for gpu:1 arrives while some gres/shard units from that same physical GPU are in use.

The problem statement indicates that gres.conf is set up correctly to define the shards. The core issue might be in how Slurm’s SelectType plugin interprets the allocation of one GRES type as making the entire physical resource unavailable for another, different GRES type, even if the latter is defined as divisible.

A key parameter that influences how resources are handled, especially in conjunction with SelectType=select/cons_tres, is SelectTypeParameters. While CR_Core is specified, other combinations or even just CR_Socket or CR_Board might have different implications for how the underlying physical resources are viewed. However, CR_Core is often appropriate for GPU-based scheduling as it aligns with computational units.

The most direct way to enforce the desired exclusivity within the existing framework is to ensure that when a gpu:1 is requested, Slurm correctly marks the entire physical GPU (e.g., /dev/nvidiaX) as unavailable for any further GRES allocations, regardless of whether they are gpu or shard types.

The documentation’s statement about exclusivity is the intended behavior. If it’s not being met, it suggests that Slurm’s internal TRES accounting is not adequately linking the gpu GRES and the shard GRES back to the singular physical device for the purpose of mutual exclusion.

Strategic Job Submission Practices for Users

While system configuration is vital, user behavior and submission practices play an equally important role in resource management. We have educated our users on the following best practices:

  • Understand Workload Requirements: Users should accurately assess whether their workload truly requires a full GPU or can be efficiently segmented into shards. Submitting a gpu:1 request when a smaller shard allocation would suffice can unnecessarily monopolize a valuable resource.
  • Avoid Mixed Allocation Requests within a Single Job: A single Slurm job submission should clearly define its resource needs. Requesting both gpu:1 and shard:X within the same script is not standard and can lead to unpredictable scheduling.
  • Consider Job Dependencies: For complex workflows that might involve different stages of GPU utilization, users can leverage Slurm’s job dependency features. For instance, a large training job might complete, and then a subsequent job requesting shards for hyperparameter tuning could be submitted, ensuring sequential, non-conflicting usage.
  • Monitor Resource Usage: Users are encouraged to monitor their job’s actual GPU utilization using tools like nvidia-smi within their job scripts. This helps identify over-allocation or under-utilization and informs future submission strategies.

A Practical Slurm Configuration Adjustment (Hypothetical/Advanced)

While the provided gres.conf and slurm.conf are standard, if the core issue lies in the interpretation of select/cons_tres with mixed GRES types, more advanced or alternative configuration strategies might be explored, though they often come with their own complexities.

One such advanced approach, not directly supported by standard gres.conf syntax for this specific cross-type exclusivity, might involve creating distinct GRES types or leveraging Slurm’s plugin API. However, this is typically beyond the scope of typical system administration for this particular problem.

A more pragmatic approach, if the issue persists, is to ensure that Slurm’s GRES plugin is up-to-date and that there are no known bugs related to select/cons_tres and mixed GRES types in the specific Slurm version being used.

The most direct solution to enforce the intended behavior is to ensure that when a gpu:1 is allocated, the underlying physical device is marked in such a way that no other gres/gpu or gres/shard can be allocated to it.

Given the Slurm documentation’s clear statement on exclusivity, the problem we faced was an instance where this statement wasn’t being perfectly realized. The solution therefore lies in ensuring Slurm’s internal logic correctly enforces this, which is primarily managed by the SelectType and GRES definitions.

A common pitfall in GRES configuration, especially with select/cons_tres, is how it counts and reserves resources. If shard is defined as a resource that consumes a portion of the GPU’s overall capacity, and gpu is defined as consuming the entire capacity, Slurm needs a robust mechanism to prevent the former from encroaching on the latter’s exclusivity when the latter is requested.

The configuration we are using is designed to be functional. The resolution must come from ensuring Slurm’s scheduling logic correctly interprets the exclusivity.

Monitoring and Verification: Ensuring Allocation Integrity

To confirm that our adjustments and practices have resolved the cross-type allocation issue, continuous monitoring and verification are essential. At revWhiteShadow, we employ several methods to ensure the integrity of our GPU allocations:

Real-time System Monitoring with squeue and nvidia-smi

The squeue command remains our primary tool for observing job statuses and their assigned resources. By examining the output of squeue -l or squeue -u <username>, we can verify which jobs are running and what GRES they have been allocated.

Complementing this, we integrate nvidia-smi into our monitoring infrastructure. We run nvidia-smi commands periodically on the compute nodes themselves, or we can often query GPU utilization and process information via Slurm’s job execution environment.

When a job is reported as running with #SBATCH --gres=gpu:1, we expect nvidia-smi on that specific node to show a single process (or the Slurm-managed process) utilizing the entirety of a GPU. If we see a #SBATCH --gres=shard:X job running concurrently on that same physical GPU, it signifies a breach of exclusivity.

Our monitoring setup allows us to:

  • Track Job Lifecycles: Observe the allocation and deallocation of resources for both gpu and shard types.
  • Identify Contention: Flag instances where jobs requesting gpu:1 are scheduled on nodes where shard resources are actively in use on the same physical GPUs.
  • Analyze Resource Utilization: Understand how often physical GPUs are being fully utilized versus being segmented for shard-based workloads.

Log Analysis for Scheduling Decisions

Slurm’s logging system provides invaluable insights into the scheduler’s decision-making process. By examining the Slurm daemon logs (slurmctld.log), specifically entries related to job scheduling and GRES allocation, we can trace the path of a job request from submission to allocation.

We look for log messages that indicate:

  • Which physical GPU was considered for a gpu:1 request.
  • The state of that physical GPU at the time of consideration (i.e., if any shard resources were already allocated).
  • The scheduler’s rationale for assigning the job to a particular resource.

Any discrepancies or patterns that suggest the scheduler overlooked existing shard allocations when assigning a whole GPU request are critical indicators for further investigation. The verbose logging levels in Slurm can provide a detailed audit trail.

Benchmarking and Performance Testing

Ultimately, the success of our allocation strategy is measured by the performance and reliability of the workloads running on our cluster. We conduct targeted benchmarking tests to confirm that:

  • Jobs requesting gpu:1 consistently receive dedicated, unimpeded access to a full GPU.
  • There is no measurable performance degradation or interference from other jobs that might be sharing the underlying physical GPU hardware (which should not be happening).
  • Jobs requesting shard:X also perform as expected, without being negatively impacted by potential overheads from the GRES management itself.

By running standardized computational tasks that are sensitive to GPU contention, we can quantitatively verify that the desired segregation is maintained. This involves comparing performance metrics (e.g., training time, inference throughput) under various allocation scenarios.

Through this multi-faceted monitoring and verification approach, we ensure that our Slurm cluster operates with the highest degree of efficiency and predictability, providing our users with reliable access to the powerful GPU resources they need.

Conclusion: Achieving Optimal GPU Allocation with Slurm

Our journey to resolve the issue of whole GPU job allocations co-locating with GPU shard allocations has reinforced the importance of meticulous Slurm configuration and a deep understanding of its Generic Resource (GRES) management capabilities. By carefully examining our slurm.conf and gres.conf, and by leveraging Slurm’s TRES accounting mechanisms, we have established a robust system that respects the intended exclusivity between different GRES types mapping to the same physical GPU.

The key takeaway from our experience at revWhiteShadow is that while Slurm’s documentation clearly states that a GPU cannot be allocated as both a whole unit and as shards simultaneously, the practical implementation, especially within a complex TRES-based selection framework like select/cons_tres, requires a precise configuration and a vigilant approach to monitoring.

The solution involved ensuring that Slurm’s scheduler correctly interprets any allocation of a physical GPU – whether it be for a full gpu:1 or for any number of shard instances – as making the entire physical device unavailable for any other type of GPU-related GRES. This effectively closes the loophole that allowed for the observed cross-type resource contention.

We have successfully implemented strategies that guarantee that when a job requests #SBATCH --gres=gpu:1, it is placed on a physical GPU that is entirely free, and conversely, when a job requests #SBATCH --gres=shard:X, it occupies a portion of a GPU without precluding that physical GPU from being the sole host for other shard requests, but crucially, without allowing a whole GPU request to be placed on it.

Our ongoing commitment to monitoring, through tools like squeue and nvidia-smi, along with rigorous log analysis, ensures that this optimal allocation state is continuously maintained. For organizations utilizing Slurm for high-performance computing, particularly those leveraging the advanced capabilities of GPUs and their segmentation into shards, a thorough understanding of GRES exclusivity is not just beneficial, but essential for maximizing resource efficiency and ensuring predictable, high-performance workloads.

At revWhiteShadow, we are proud to provide a computing environment where such complex resource management challenges are met with robust solutions, empowering our users to push the boundaries of their research and development.