AMD MI300A APU: Unified Chiplet Design

• MI300A APUs are heterogeneous processors that combine Zen4 CPU cores, GPU compute units, and high-bandwidth memory into one unified architecture, enabling seamless data and task movement.
• They feature an advanced chiplet design with unified physical memory, eliminating host–device transfers and reducing memory costs by up to 44%.
• They support accelerator-level parallelism through dedicated matrix engines and dynamic scheduling, significantly boosting ML and scientific workloads performance.

The AMD Instinct MI300A Accelerated Processing Unit (APU) is a heterogeneous, chiplet-based processor integrating CPU cores, GPU compute units, and high-bandwidth memory (HBM3) into a unified package, targeting exascale-class high-performance computing (HPC) deployments. The MI300A enables seamless data and task movement by leveraging unified physical memory (UPM), sophisticated interconnect (Infinity Fabric), advanced matrix engines, and comprehensive system software support. These architectural and system innovations position MI300A APUs at the forefront of memory bandwidth–intensive, parallel, and multi-accelerator workloads in leadership-class systems such as El Capitan.

1. Chiplet Architecture and Unified Memory Integration

The MI300A APU consists of multiple “Core Complex Dies” (CCDs)—hosting Zen4 CPU cores—and several “Accelerator Complex Dies” (XCDs), each incorporating many GPU compute units. This ensemble is connected to eight HBM3 stacks providing a total of 128 GB high-bandwidth memory with theoretical local bandwidth in excess of 5.3 TB/s per package. In addition, a 256 MB Infinity Cache, structured into 128 slices, sits alongside main memory as a shared, memory-side cache offering bandwidth up to 17.2 TB/s, a triple increase over HBM3 alone (Wahlgren et al., 18 Aug 2025). CPU and GPU subsystems address a common, physically unified memory pool. Virtual memory management for each processor is governed by replicas of a shared page table, which are kept coherent via Linux’s HMM service.

This tightly unified architecture eliminates traditional host–device boundaries: data no longer requires explicit copying or migration between CPU DRAM and discrete GPU memory, reducing total memory cost by up to 44% and simplifying software development (Wahlgren et al., 18 Aug 2025). On MI300A, memory can be allocated using either on-demand methods (e.g., malloc), which trigger page faults upon first-touch, or up-front allocators such as hipMalloc—where contiguous allocation also ensures fewer TLB misses and improved Infinity Cache utilization.

Component	Capacity/Bandwidth	Functional Role
HBM3 (8 stacks)	128 GB, 5.3 TB/s	Unified memory for CPU/GPU
Infinity Cache	256 MB, 17.2 TB/s	Shared, memory-side cache
CCD (CPU)	8 cores/die, 3 dies	Zen4 24-core CPU subsystem
XCD (GPU)	~38 CUs/die, 6 dies	228 GPU compute units per package

2. Accelerator-Level Parallelism and Hierarchical Parallel Execution

MI300A is specifically designed to expose diverse hardware parallelism paradigms. Accelerator-level parallelism (ALP) (Hill et al., 2019), refers to concurrent execution across heterogeneous accelerators—CPU, GPU, and specialized engines (e.g., NPUs, DSPs). Each accelerator operates at a distinct performance and energy efficiency profile, with total throughput modeled as $P_{total} = \sum_{i=1}^N P_i$ for N hardware engines. ALP sits atop the hierarchy, complementing data-level parallelism (DLP) and thread-level parallelism (TLP) within the CPU and GPU complexes.

Optimal system operation requires advanced scheduling strategies: dynamic runtime allocation, leveraging unified abstraction models (OpenCL, HIP, OpenMP 5.2), and tailored data layout transformations. These approaches mitigate resource underutilization and balance workload partitioning across accelerators for both performance and cost goals. Formally, the system design can be expressed as:

$$\max_\eta \frac{\sum_{i=1}^N P_i}{\sum_{i=1}^N E_i}$$

subject to area, execution time, and resource constraints.

3. Matrix Engine Computation and ML Workload Acceleration

MI300A integrates Matrix Core Engines (MCEs) dedicated to efficient matrix fused multiply-add (MFMA) instructions (Kurzynski et al., 30 Jan 2025). Each Compute Unit (CU) houses four MCEs, each capable of executing instructions of the form $D = C + A \times B$ for matrix fragments (e.g., $D$ and $C$ as $4\times4$, $A$ as $4\times1$, $B$ as $1\times4$). MCEs operate concurrently with other execution units, supporting deep learning kernels in frameworks such as PyTorch and TensorFlow.

The simulation fidelity afforded by tools such as gem5—with cycle-resolved model tables for MFMA latency—enables precise modeling of throughput improvements for ML workloads and system co-design. Adjustments to MFMA timing parameters directly inform hardware trade-offs and future accelerator architecture. Concurrent execution of MFMA and scalar operations within CUs ensures that MI300A APUs are highly suited to throughput-centric ML tasks.

4. Unified Physical Memory: Allocation, Latency, and Application Porting

MI300A’s UPM architecture removes software-managed migration costs and page fault bottlenecks inherent in Unified Virtual Memory (UVM) (Wahlgren et al., 18 Aug 2025). On MI300A, both host and device access the same physical memory, with allocation overhead minimized and coherence maintained through hardware support. Memory latency progresses through a distinct hierarchy (on GPU: L1 ≈ 57 ns, L2 ≈ 108 ns, Infinity Cache ≈ 218 ns, HBM ≈ 350 ns; on CPU: L1 ≈ 1 ns, HBM ≈ 241 ns).

System software handles page faults efficiently, with CPU and GPU fault throughput up to 3.7 M and 1.1 M pages/s respectively. Strategies such as CPU pre-faulting transform expensive GPU page faults into minor events. Optimum TLB coverage is achieved via up-front allocation (hipMalloc), yielding an order-of-magnitude reduction in TLB misses and favoring cache utilization.

Porting explicit memory management codes to leverage UPM involves merging CPU/GPU buffers, restructuring data access for concurrency (e.g., double buffering), refactoring static allocations to dynamic, and optimizing standard library container allocation (e.g., replacing std::allocator with hipMalloc). These steps eliminate redundant buffers, reduce peak memory use, and can yield performance parity or improvements over explicit management, as shown in Rodinia suite benchmarks (Wahlgren et al., 18 Aug 2025).

5. High-Bandwidth Inter-APU Communication via Infinity Fabric

MI300A nodes in leadership-class supercomputers (El Capitan, etc.) integrate four APUs in a mesh topology using Infinity Fabric (Schieffer et al., 15 Aug 2025). Each package connects to others using two xGMI3 links per peer (16 b @ 32 GT/s), enabling an aggregated bandwidth of 128 GB/s per direction. Measured bidirectional GPU-initiated copy bandwidth reaches 103–104 GB/s, approximately 81% of theoretical peak.

Benchmarking reveals latency characteristics for various modes of data movement: local HBM (CPU 240 ns, GPU 346 ns), remote APU via Infinity Fabric (CPU 500 ns, GPU 690 ns). Programming models for inter-APU communication include HIP (hipMemcpy, optimized for large transfers), MPI (low-latency for small messages, CPU staging), and RCCL (GPU collectives, near-peak bandwidth for large messages, less sensitive to allocator). Careful matching of buffer allocators (hipMalloc versus system malloc) and offload engine utilization maximizes throughput; RCCL is preferred for collective operations as it outpaces MPI latency by up to 38× in large message contexts.

Programming Model	Small Msg Latency	Large Msg Bandwidth	Allocator Sensitivity
MPI (CPU staging)	~1.9 μs	~68 GB/s	High (hipMalloc req.)
HIP APIs	~20 μs	~90 GB/s	Moderate
RCCL	~20 μs	~88–90 GB/s	Low

Case studies (Quicksilver, CloverLeaf) demonstrate the practical impact of allocator selection, XNACK tuning, and RCCL adoption: runtime improvements range from 5–11% (QuickSilver) to 1.5–2.2× (CloverLeaf) for optimized communication.

6. Application Characteristics and Performance Optimization

MI300A APUs excel at memory- and compute-bound workloads. In accelerated matrix multiplication, recursive block decomposition assigns subproblems to the most appropriate engine—CPU for small tiles (SGEMM via ATLAS/GotoBLAS), integrated GPU for mid-sized blocks, and external GPUs for largest tiles—with OpenCL or similar abstractions handling task scheduling (D'Alberto, 2012). Peak performance scales from 90 GFLOPS (CPU only) to 200 GFLOPS in hybrid mode.

Memory-bound algorithms such as PERMANOVA benefit disproportionally from GPU compute: shared HBM3 bandwidth delivers GPU Triad throughput (~3.0 TB/s) that is an order of magnitude higher than CPU (~0.2 TB/s), resulting in more than 6× speedup for brute force kernel execution on the GPU, further doubled by CPU SMT usage (Sfiligoi, 7 May 2025). For hybrid codes in scientific computing and CFD, directive-based offload (OpenMP 5.2 unified_shared_memory) simplifies incremental acceleration, as seen in OpenFOAM, with a reported 4× performance increase over state-of-the-art discrete systems (Tandon et al., 1 May 2024).

At exascale, MI300A APUs provide the raw scalability for implicit kinetic plasma simulations with trillions of particles. Features essential to this domain—GPU-optimized compute kernels, dynamic particle control (coalescence/splitting via GPU), in-situ physics-aware data compression (GMM, with >1000× reduction)—are unified within the shared-memory environment, permitting MPI workloads on up to 32,768 packages (Markidis et al., 28 Jul 2025).

7. Implications and Future Trends in HPC System Design

The architecture of MI300A APUs, particularly the integration of unified physical memory, advanced chiplet interconnect, and diverse accelerator-level parallelism, represents a shift in HPC system design priorities. Key implications include:

• Reduction in system memory cost and programming complexity—removal of host–device data copies, software migration, and buffer duplication.
• Enhanced application portability and incremental offloading—hardware-agnostic development paths via directive-based models.
• Scalability for memory and task–bandwidth–intensive scientific applications (magnetospheric PIC, large-volume Monte Carlo), including effective scaling to tens of thousands of packages.
• Architecturally driven optimization strategies—allocator selection, page fault pre-faulting, cache utilization.
• In-situ benchmarking and simulation capabilities (e.g., gem5 with MCE/MFMA modeling) support hardware–software co-design methodologies for future workloads.

A plausible implication is that tightly integrated, accelerator-rich packages with unified physical memory and high-bandwidth mesh interconnects will become standard among leadership-class HPC architectures. This trend suggests increasingly seamless transitions between CPU and GPU compute, sophisticated memory and task scheduling, and continued migration away from software-managed memory hierarchies toward physically unified pools—potentially informing the design of next-generation exascale and AI supercomputers.