Choosing the Right HPC Solution: Velda vs Slurm

Both Velda and Slurm are powerful options for managing HPC clusters with similar command-line interfaces for job execution. Users already familiar with Slurm will find Velda's transition seamless, while gaining access to a modern, cloud-native architecture designed for ML and AI workloads.

Resource Management and Deployment Model

Slurm’s Traditional Node-Based System

Slurm operates on a legacy HPC model with fixed-size clusters and traditional node-level resource management. It lacks proper isolation among jobs regarding resources and networking, creating significant inefficiencies with modern, heterogeneous workloads. Users must navigate complex configurations to maximize cluster capacity and efficiency.

Velda’s Cloud-Native Architecture

Velda leverages multiple backends to allocate compute resources: statically managed nodes in traditional data centers, virtual machines from your selected clouds, or pods from Kubernetes. Multi-cloud scaling is available out of the box.

Velda supports a variety of instance configurations and dynamically provisions compute based on workload demands. This improves cost efficiency and cluster capacity to meet your specific budget and performance requirements.

Learn more about Velda compute pools

Development Experience

Slurm: Built for Running Jobs

Slurm was not built for daily development workflows. It lacks native support for development instances, forcing teams to choose between risky shared login nodes or error-prone workarounds like submitting "sleep" jobs and using direct SSH. Debugging jobs running on different nodes is challenging, and running services is poorly supported. Developers face restrictions on critical actions like installing system packages or running Docker commands. When jobs are queuing, developers must stop iterating on the same codebase, significantly slowing down development cycles.

Velda: Developer-First Platform

Velda puts the developer experience first. Developers get full access to their dev-container and can customize it to their needs, including installing packages and running commands that require system privileges like Docker (excluding system kernel changes). Instantly onboard new users by cloning instances from templates tailored to your team. When submitting jobs, developers can take an instant snapshot of the entire environment for consistent and reproducible results while continuing to iterate even when jobs are running or queuing.

Job Scheduler & Workload Management

Slurm offers powerful workload management features, thanks to its long-standing industry adoption. Velda provides comparable and enhanced features specifically tailored for common ML workflows, model training, and evaluation.

Built for Model Training & AI Workloads

Velda is built for the full lifecycle of machine learning and natively supports advanced scheduling features, including:

• Advanced GPU Scheduling Options
  • Gang scheduling: start all workloads when all resources are ready
  • Network-topology-aware cloud instance allocation, extending your resource allocation backend with specific topology options (e.g., EC2 Placement Groups in AWS, Compact Placement in Google Cloud, or custom config in KQueue)
• Define workflows that involve heterogeneous steps (e.g., data processing, training, evaluation, and model deployment)
• Platform-agnostic: supports PyTorch, JAX, Ray, and other ML frameworks
• Start model serving and service development in one command, accessible via custom DNS names. See vrun is all you need

Workload Isolation

Slurm provides basic process isolation through Linux user accounts and relies on shared filesystems for data access. This approach creates potential security vulnerabilities and makes it difficult to ensure complete workload separation, especially in multi-tenant environments or when workloads require different system dependencies. Direct SSH access to compute nodes for debugging creates additional security and resource isolation risks.

Velda implements per-user, per-instance containers. Each user's workload runs in a completely isolated environment, addressing the challenges of secure multi-tenancy. Users can only connect to environments associated with their instances. Resource pools can be configured with isolation tailored to specific needs: containers, micro-VMs (Kata), VMs, or bare-metal for whole-node access.

Integrations with Other Job Frameworks

Velda provides direct integrations with popular job orchestration frameworks like Ray, enabling you to run distributed jobs without managing underlying infrastructure.

Most job frameworks (Airflow, Celery, etc.) don't provide cluster management out of the box, and those that do (e.g., Ray) come with restrictions due to various customizations in packaging, observability, and Kubernetes manifests.

Velda enforces a consistent standard on environment management by cloning your entire dev stack with simple parameters to scale. This enables Velda to directly integrate with other job managers and provides a consistent experience running locally versus in the cloud.

While some community integrations exist for Slurm, they rely on specific restricted Slurm cluster setups, limiting their support and usability.

Enterprise Features and Compliance

Modern organizations require robust access controls, audit logging, and compliance capabilities. Slurm's basic accounting and limited Role-Based Access Control (RBAC) features often fall short of enterprise security requirements, relying heavily on third-party tools or custom integrations like syncing SSO with LDAP directories.

Velda provides comprehensive enterprise features out of the box, including SSO/SAML integration, detailed usage reporting, and granular RBAC policies. It also includes advanced features for optimizing resource usage and costs, supporting efficient workload management. Native integration with existing security infrastructure ensures seamless adoption in regulated environments.