Tensara is a platform for GPU programming challenges, allowing developers to write efficient GPU kernels and compare their solutions with others.
Features
Triton submission support
Allows participants to submit solutions developed using Triton, enhancing GPU programming flexibility.
Rating & leaderboard system
Tracks and displays participants' performance through live rankings and a rating system.
Test result streaming with SSE
Provides real-time feedback on test results using Server-Sent Events, keeping participants informed instantly.
PyTorch-based test cases
Utilizes PyTorch for testing solutions, enabling robust validation through a widely-used machine learning library.
3D/4D Tensor matmul problems
Offers complex matrix multiplication problems involving 3D and 4D tensors to challenge and improve kernel optimization skills.
CLI interface for platform
Provides a command-line interface for interacting with the platform, simplifying access and submission processes for developers.
Competitive Problems
Offers a range of computational problems categorized by difficulty and specific tags, allowing users to challenge their understanding of deep learning concepts.
Tag-based Search
Enables users to filter problems based on specific tags like Convolution, Normalization, or Activation Functions, making it easier to find tasks relevant to their learning needs.
GitHub Integration
Allows users to sign in using their GitHub accounts, making it convenient to manage submissions and track progress directly linked to their developer profile.
Problem Tracking
Enables users to track and manage problems effectively, possibly for coding, bug tracking, or similar applications.
FLOPS Calculation
Allows users to compute total floating point operations for various problem types using a generalized function, helping in performance measurement across different input sizes.
Starter Code Generation
Dynamically generates starter code for different problem types, providing a simple interface with full flexibility for implementation.
Precise Runtime Measurement
Uses CUDA events to measure kernel execution times accurately, ensuring precise benchmark results.
Convergence-Based Benchmarking
Utilizes a coefficient of variation approach to collect enough samples for a reliable benchmark, ensuring fair and accurate performance comparisons.