Heterogeneous architectures

What Makes an Architecture “Heterogeneous”?

In traditional homogeneous systems, most computation is performed by one type of processor (typically general-purpose CPUs). In heterogeneous architectures, different kinds of processors and accelerators coexist and collaborate in the same system, each specializing in particular workloads.

Key aspects that make an architecture heterogeneous in an HPC context:

• Multiple processor types
• General-purpose CPUs (e.g., x86, ARM)
• GPUs and other manycore accelerators
• Specialized devices (FPGAs, AI accelerators, DPUs/IPUs, vector engines)
• Diverse memory and interconnect characteristics
• Different memory technologies (e.g., DDR, HBM, GDDR, NVRAM)
• Varied bandwidth, latency, capacity, and access models
• Device-local memories plus shared host memory
• Multiple programming and execution models
• CPU code, GPU kernels, offloaded regions, dataflow graphs, etc.
• Different compilers, libraries, and runtimes orchestrating devices

Heterogeneous architectures aim to match the right compute resource to the right type of work, trading the simplicity of homogeneity for potential gains in performance, energy efficiency, and cost.

Primary Forms of Heterogeneity in Modern HPC

CPU–GPU Systems

The most widespread heterogeneous design in current and emerging supercomputers pairs multi-core CPUs with one or more GPUs per node.

Typical characteristics:

• CPUs
• Few to tens of powerful cores
• Good at control-heavy, latency-sensitive, and sequential tasks
• Run OS, MPI ranks, communication libraries, and orchestration
• GPUs
• Thousands of simpler cores focused on throughput
• Excellent at highly parallel, compute- and bandwidth-intensive kernels
• Often attached via PCIe or high-bandwidth interconnects (NVLink, etc.)

Common node layouts:

• 1 CPU + multiple GPUs (e.g., 1x64-core CPU + 4 GPUs)
• 2 CPUs + multiple GPUs (e.g., 2xCPU + 4–8 GPUs)
• GPU-only compute nodes in some architectures (minimal CPU, many GPUs)

This architecture underpins many exascale and pre-exascale systems because it offers a favorable ratio of performance per watt and performance per cost for large-scale simulations and AI workloads.

CPU–FPGA and Custom Accelerator Systems

FPGAs and other reconfigurable or custom accelerators add another dimension of heterogeneity:

• FPGAs (Field-Programmable Gate Arrays)
• Logic can be reconfigured to implement tailored pipelines
• Extremely efficient for specific dataflows (e.g., streaming, custom arithmetic)
• Lower clock speeds but very high concurrency and fine-grained control
• Often programmed using high-level synthesis (HLS), hardware description languages, or special frameworks
• Custom ASIC accelerators
• Application-specific chips (e.g., AI tensor cores, NPUs, TPUs) integrated into HPC platforms
• Optimized for particular operations (e.g., matrix multiplies, convolutions)

These devices tend to be deployed when energy efficiency or specialized functionality justifies the added design and programming complexity.

Heterogeneous Memory and Storage

Modern nodes often mix multiple memory and storage technologies:

• CPU DRAM + HBM (High-Bandwidth Memory) on accelerators
• GPUs with on-package HBM provide extremely high bandwidth for device-local data
• CPU-attached DRAM offers larger capacity but lower bandwidth
• Non-Volatile Memory (NVM)
• Persistent memory modules (e.g., Intel Optane-class devices)
• Layer between DRAM and disk: larger capacity, slower than DRAM, faster than SSDs
• Tiered storage inside nodes and across the system
• On-node NVMe SSDs, burst buffers, archival storage tiers

The result is not only heterogeneous compute but also heterogeneous memory hierarchies, which strongly affect performance and data placement strategies.

Network and Offload Engines

HPC networks are also becoming heterogeneous:

• Smart NICs and DPUs (Data Processing Units)
• Offload communication, encryption, storage protocols, and sometimes MPI progress
• Free CPU cycles and reduce communication overheads
• Network-attached accelerators
• GPUs or FPGAs closely coupled to network interfaces for overlapping communication and computation

This adds another layer where work can be offloaded and where data can be transformed in flight.

Architectural Patterns in Heterogeneous HPC Systems

Node-Level Heterogeneity

Within a node, you often see:

• Host–device model
• CPU is the host, orchestrating work
• Accelerators are devices receiving data and kernels
• Explicit data transfers (e.g., cudaMemcpy) or unified memory abstractions
• NUMA and multi-socket complexities
• CPUs with multiple memory domains and local vs remote memory
• Different PCIe root complexes connecting to different GPUs
• Need to align CPU cores, memory, and accelerator placement (affinity)

Node-level heterogeneity primarily affects how applications:

• Partition work between CPU and accelerators
• Place threads and MPI processes relative to devices
• Manage data movement and locality inside the node

System-Level Heterogeneity

At system scale, heterogeneity can appear as:

• Mixed-node clusters
• Some nodes CPU-only, some with GPUs, some with FPGAs
• Scheduling must understand and allocate the right node types
• Generational heterogeneity
• Clusters with multiple GPU generations or CPU microarchitectures
• Performance characteristics differ across nodes, affecting load balancing
• Heterogeneous interconnect capabilities
• Nodes with NVLink, others with only PCIe
• Nodes with dedicated GPU-direct networking vs those without

Applications need to cope with varying capabilities and adapt strategies accordingly, sometimes at runtime.

Programming and Runtime Challenges

Managing Multiple Execution Models

Heterogeneous systems typically require:

• Different programming models for different devices
• CUDA, HIP, SYCL, OpenCL, OpenACC, OpenMP offload, vendor-specific APIs
• Library-based offload (e.g., cuBLAS, oneMKL, rocBLAS)
• Hybrid composition
• MPI across nodes; GPU offload within nodes; possibly OpenMP threads on CPUs
• Task-based or graph-based execution models to schedule heterogeneous work

Key challenge: unifying or abstracting these models enough to keep applications maintainable while still achieving performance.

Data Movement and Memory Coherence

On heterogeneous nodes, data is often physically separate:

• Device-local memory vs host memory
• Explicit vs implicit data movement
• Coherency models (some architectures support cache coherence, others don’t)

HPC applications must:

• Decide which data resides where, and when it moves
• Overlap data transfers with computation
• Minimize unnecessary copies, especially over PCIe or network links

Future architectures increasingly offer:

• Unified virtual address spaces
• Hardware-managed memory migration
• Direct GPU–GPU and GPU–NIC paths (e.g., GPU-direct technologies)

These reduce programming burden but introduce new performance tuning dimensions.

Load Balancing Across Heterogeneous Resources

Load balancing becomes more complex:

• Different devices may run the same algorithm at different speeds
• Compute capability can differ per node or per device
• Some devices are better at specific kernels (e.g., sparse vs dense operations)

Approaches include:

• Static partitioning based on benchmarked ratios (e.g., GPU does 90% of work, CPU 10%)
• Dynamic task-based runtimes that schedule work chunks to any available resource
• Auto-tuning and performance models to select kernel variants per device

Designing algorithms that map well onto multiple device types is a key research and engineering challenge.

Portability and Software Ecosystem

Cross-Platform Programming Approaches

To cope with many heterogeneous architectures, the ecosystem is moving towards:

• Portability layers and abstraction frameworks
• SYCL, Kokkos, RAJA, Alpaka, OCCA, and similar libraries
• Directive-based models (OpenMP, OpenACC offload)
• Backend-switching approaches (e.g., one code, CUDA/HIP/SYCL backends)
• Vendor-neutral APIs
• Efforts to standardize core functionality so that one codebase runs on CPUs, GPUs from different vendors, and other accelerators

Trade-off:

• Higher portability and easier maintenance
• Potential risk of not exploiting vendor-specific features to the maximum degree

Heterogeneity-Aware Runtimes and Libraries

Future HPC software stacks are increasingly:

• Runtime-driven
• Task graphs, dependency analysis, locality-aware schedulers
• Automatic mapping of tasks to devices based on heuristics or models
• Library-centric
• Applications delegate performance-critical operations to tuned libraries
• Libraries internally decide how to use CPUs, GPUs, and other accelerators

Examples:

• Multi-backend linear algebra and FFT libraries
• Communication libraries optimized for GPU buffers and in-network processing
• Workflow engines able to dispatch tasks across heterogeneous resources

Energy Efficiency and Heterogeneous Design

A major driver of heterogeneity is the need to stay within power and energy budgets, especially at exascale.

Heterogeneous strategies for efficiency:

• Offload high-throughput work to accelerators with better performance-per-watt
• Use specialized units (tensor cores, low-precision units, fixed-function engines) for dominant kernels
• Exploit DVFS (dynamic voltage and frequency scaling) differently on CPUs vs accelerators
• Power-aware scheduling: shifting work among devices to meet performance and energy targets

Future systems may dynamically choose between device types or precision levels to balance accuracy, runtime, and energy.

Design and Algorithmic Implications

Algorithm Redesign for Heterogeneity

Many traditional algorithms assume:

• Uniform compute cost per operation
• Homogeneous memory and communication costs

Heterogeneous architectures break these assumptions, pushing developers to:

• Reformulate algorithms to increase parallelism on accelerators
• Reduce or reorganize data movement between CPUs, GPUs, and memory tiers
• Use asynchronous, pipeline-style execution (streaming, task graphs)
• Exploit mixed-precision and specialized operations while preserving result quality

In some cases, completely new algorithmic families are being developed specifically to exploit GPU tensor cores, FPGAs, or near-memory processing.

Resilience and Heterogeneous Redundancy

As systems grow and diversify:

• Failure modes vary among CPUs, GPUs, FPGAs, and networks
• Different components may have different reliability and error characteristics

Heterogeneous designs can enable:

• Redundant computation on diverse devices for verification
• Checkpointing or redundancy tailored to which component is more failure-prone
• Selective replication of critical kernels on more reliable components

Research Directions and Emerging Concepts

Heterogeneous architectures are a fast-moving target. Some actively explored directions include:

• Near-memory and in-memory computing
• Computation closely integrated with memory to reduce data movement
• Processing-in-memory (PIM) for bandwidth-bound workloads
• Heterogeneous manycore CPUs
• “Big.LITTLE” concepts scaled up for servers (high-performance + low-power cores)
• Combining general-purpose cores with specialized vector/tensor units on the same die
• Tightly integrated CPU–GPU and chiplet designs
• Shared coherent memory between CPU and GPU
• Chiplet-based systems combining different process technologies and IP blocks
• AI-augmented runtimes
• Machine learning models predicting performance and adaptively tuning resource usage
• Automatic device selection, data placement, and kernel configuration
• Heterogeneity across sites and clouds
• Hybrid on-premise clusters and cloud resources with diverse accelerators
• Meta-scheduling across heterogeneous facilities

Practical Implications for Future HPC Users

For an HPC beginner preparing for future heterogeneous systems, it is useful to:

• Expect to target multiple architectures over the lifetime of a code
• Learn at least one portable offload or abstraction model (e.g., OpenMP offload, SYCL, Kokkos)
• Develop an understanding of:
• How compute, memory, and communication differ across device types
• How to reason about data movement as a first-class performance factor
• How to design experiments that compare architectures fairly

Heterogeneous architectures will continue to evolve, but the core challenge remains stable: mapping algorithms and data efficiently onto diverse hardware resources while keeping software maintainable and portable.