{"slug": "cuda-like-programming-of-cerebras-wse", "title": "CUDA-like programming of Cerebras WSE", "summary": "A high-fidelity architectural simulator for the Cerebras CS3 Wafer-Scale Engine (WSE) has been developed, modeling 720,000 processing elements on a 2D mesh to enable performance analysis and software development. The simulator uses a CUDA-like programming model and a hybrid performance/functional execution track to balance accuracy and speed, supporting a full toolchain from a Python DSL to a custom 32-bit ISA binary.", "body_md": "A high-fidelity architectural simulator for the Cerebras CS3 Wafer-Scale Engine (WSE). This project models the hardware architecture, interconnects, and execution environment to enable performance analysis and software development for massively parallel 2D mesh architectures.\n\nThe Cerebras-Sim is designed to model the CS3 WSE, featuring a massive array of 720,000 processing elements (PEs). It provides a full-stack simulation environment, from a high-level Python DSL down to a custom 32-bit ISA binary.\n\n**Performance Analysis**: Estimate total runtime and identify bottlenecks using a hybrid performance/functional model.** Software Development**: Verify kernel correctness via a CUDA-like programming model before deploying to hardware.** Architectural Exploration**: Model the impact of mesh bisection bandwidth, latency, and SRAM constraints.\n\n**Processing Element (PE)**: Each core implements an 8-wide SIMD unit, vector registers, and a private** 48KB local SRAM**.** Interconnect**: A 2D Mesh (800x900) where communication occurs via`SEND`\n\n/`RECV`\n\nprimitives and global address space abstractions.**Memory Hierarchy**:** Local SRAM**: Private high-speed memory per PE (analogous to CUDA Shared Memory).** Weight Server**: External DRAM accessed via a global address space for large-scale model weights and data.\n\n**Host-Device Interface**: A driver model implementing a command queue (`CS3Queue`\n\n) and memory movement (`cs3_memcpy`\n\n).\n\nThe simulator employs a **Bulk Synchronous Parallel (BSP)** model, dividing execution into discrete \"supersteps\":\n\n**Compute**: PEs perform local SIMD operations.** Communicate**: PEs exchange data across the mesh or with the Weight Server.** Synchronize**: A global barrier (`SYNC`\n\n) aligns the execution state.\n\nTo balance accuracy and speed, the simulator uses a **hybrid execution track**:\n\n**Performance Track (Global)**: All PEs are tracked for cycle counts and timing.** Functional Track (Sampled)**: A stochastic sampling strategy is used where only a subset of blocks is fully simulated functionally to verify correctness.\n\nThe project implements a complete toolchain:\n`Python DSL`\n\n`Tungsten-IR`\n\n`ISA Binary`\n\n`Simulator`\n\nKernels are written in a CUDA-like Python DSL. For example, a simple SAXPY (\n\n``` python\n@cs3_kernel(block_w=16, block_h=16)\ndef saxpy_kernel(ctx):\n    # Load inputs from global memory (Weight Server)\n    x = ctx.load_global(None, 0)\n    y = ctx.load_global(None, 4)\n    \n    # Compute: z = 2.0 * x + y\n    z = 2.0 * x + y\n    \n    # Store result back to global memory\n    ctx.store_global(None, 8, z)\n```\n\n**Frontend**: A CUDA-like DSL embedded in Python using`@cs3_kernel`\n\ndecorators.**Intermediate Representation (Tungsten-IR)**: A dataflow-centric IR mapping compute nodes and synchronization points.** Compiler Backend**:** Mapping & Scheduling**: Assigns IR nodes to the physical 2D mesh and manages the SRAM budget.** Assembler**: Emits the final 32-bit binary stream.\n\n**Simulator Engine**: A Python-based engine that decodes the ISA and drives the hardware model.\n\nInstead of exhaustive packet-level simulation, the system uses a **latency-and-bandwidth-aware abstract model**:\n\n-\n**Latency**: Calculated based on physical Manhattan distance:$$\\text{Latency}_{\\text{op}} = \\text{Base Latency} + (\\text{Manhattan Distance} \\times \\text{Hop Latency})$$ -\n**Bandwidth & Congestion**: The simulator enforces a** Bisection Bandwidth Constraint**. If total bytes transferred per superstep exceed network capacity, a congestion multiplier is applied to \"stretch\" the superstep duration.\n\n| Component | Status | Details |\n|---|---|---|\nISA Decoder |\n✅ Complete | Full implementation of Compute, Mesh, Control, System, DSD, and Global memory opcodes. |\nHardware Model |\n✅ Functional | Core logic, SRAM, 2D Mesh, and Host-Device IO are implemented. |\nCompiler |\n✅ Functional | AST parsing, IR generation, register allocation, and assembly are operational. |\nSimulation Engine |\n✅ Functional | Hybrid Performance/Functional tracks and BSP scheduling are implemented. |\nAdvanced Mapping |\n🚧 In Progress | Optimizing spatial mapping for complex kernels. |\nWeight Server |\n🚧 In Progress | Integration with external weight servers for real-world model weights. |\n\nThe project uses a **Dual-Execution** strategy to verify the compiler:\n\n-\n**Python Path**: Executes the kernel as a Python function via`KernelContext`\n\n(Golden Reference). -\n**Binary Path**: Compiles the kernel$\\rightarrow$ executes the resulting binary on the`BSPScheduler`\n\n. -\n**Comparison**: Bit-exact comparison of final memory states.\n\nTo run integration tests:\n\n```\npython3 -m unittest discover tests/integration\n```\n\n", "url": "https://wpnews.pro/news/cuda-like-programming-of-cerebras-wse", "canonical_source": "https://github.com/greg1232/cerebras-py-sim", "published_at": "2026-06-15 07:32:48+00:00", "updated_at": "2026-06-15 07:41:34.506471+00:00", "lang": "en", "topics": ["ai-chips", "ai-infrastructure", "developer-tools", "machine-learning"], "entities": ["Cerebras", "CS3 WSE", "Wafer-Scale Engine", "Cerebras-Sim", "Tungsten-IR", "SAXPY"], "alternates": {"html": "https://wpnews.pro/news/cuda-like-programming-of-cerebras-wse", "markdown": "https://wpnews.pro/news/cuda-like-programming-of-cerebras-wse.md", "text": "https://wpnews.pro/news/cuda-like-programming-of-cerebras-wse.txt", "jsonld": "https://wpnews.pro/news/cuda-like-programming-of-cerebras-wse.jsonld"}}