11.1 Processor architectures (CPUs, GPUs, accelerators)

Processor architectures are the backbone of Exascale Computing. CPUs, GPUs, and accelerators each bring unique strengths to the table, enabling massive computational power for complex scientific problems. Understanding these architectures is crucial for designing efficient systems that can handle the demands of Exascale Computing.

From instruction-level parallelism in CPUs to the massively parallel processing of GPUs, each architecture offers distinct advantages. Accelerators provide specialized processing for targeted workloads. As we push towards Exascale, future trends like near-memory and neuromorphic computing promise even greater capabilities.

Processor architectures overview

• Processor architectures are a critical component of Exascale Computing, as they determine the capabilities and performance of the computing systems used for large-scale simulations and data analysis
• Understanding the differences between CPUs, GPUs, and accelerators is essential for designing efficient and scalable Exascale systems that can handle the massive computational demands of complex scientific and engineering problems
• The choice of processor architecture has significant implications for power consumption, memory bandwidth, and programmability, which are key considerations in Exascale Computing

CPUs vs GPUs vs accelerators

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• CPUs (Central Processing Units) are general-purpose processors designed for a wide range of computing tasks, offering flexibility and compatibility with existing software (x86, ARM)
• GPUs (Graphics Processing Units) are specialized processors originally designed for graphics rendering but have evolved to handle parallel computing workloads, providing high throughput for data-parallel tasks (NVIDIA, AMD)
• Accelerators are purpose-built processors designed to accelerate specific types of workloads, such as machine learning or signal processing, and can offer significant performance gains for targeted applications (Google TPU, Intel Xeon Phi)

CPU architectures

• CPU architectures have evolved to improve performance through various techniques, such as instruction-level parallelism, out-of-order execution, and multi-core designs
• These advancements have enabled CPUs to handle more complex and diverse workloads, making them a critical component in Exascale Computing systems
• However, the scalability of CPU architectures for Exascale Computing is limited by power consumption and memory bandwidth constraints

Instruction-level parallelism

• Instruction-level parallelism (ILP) is a technique that allows multiple instructions to be executed simultaneously within a single CPU core
• ILP is achieved through techniques such as pipelining, which overlaps the execution of multiple instructions, and superscalar execution, which allows multiple instructions to be issued and executed in parallel
• Examples of ILP techniques include:
  • SIMD (Single Instruction, Multiple Data) extensions (SSE, AVX)
  • VLIW (Very Long Instruction Word) architectures (Itanium)

Out-of-order execution

• Out-of-order execution is a technique that allows a CPU to execute instructions in a different order than they appear in the program, based on the availability of data and resources
• This technique helps to minimize pipeline stalls and improve overall performance by allowing the CPU to continue executing instructions while waiting for data dependencies to be resolved
• Examples of out-of-order execution include:
  • Tomasulo's algorithm
  • Scoreboarding

Branch prediction

• Branch prediction is a technique used by CPUs to predict the outcome of conditional branches in a program, allowing the processor to speculatively execute instructions along the predicted path
• Accurate branch prediction can significantly improve performance by reducing pipeline stalls caused by branch mispredictions
• Examples of branch prediction techniques include:
  • Static branch prediction (always taken, always not taken)
  • Dynamic branch prediction (two-bit saturating counter, perceptron-based)

Speculative execution

• Speculative execution is a technique that allows a CPU to execute instructions before it is known whether they will be needed, based on branch predictions or other heuristics
• If the speculation is correct, the results are committed, and execution continues normally; if the speculation is incorrect, the results are discarded, and execution resumes from the correct path
• Examples of speculative execution include:
  • Branch prediction-based speculation
  • Load-store speculation

Multi-core designs

• Multi-core designs involve integrating multiple CPU cores onto a single chip, allowing for parallel execution of multiple threads or processes
• Multi-core architectures have become prevalent in Exascale Computing systems, as they offer improved performance and energy efficiency compared to single-core designs
• Examples of multi-core CPU architectures include:
  • Intel Xeon (up to 28 cores)
  • AMD EPYC (up to 64 cores)

Cache hierarchies

• Cache hierarchies are used in CPU architectures to bridge the performance gap between the processor and main memory by storing frequently accessed data in faster, smaller memory closer to the processor
• Modern CPUs typically employ multiple levels of cache (L1, L2, L3), with each level offering a different balance of capacity and access latency
• Examples of cache hierarchy designs include:
  • Inclusive cache hierarchies (all data in lower levels is also present in higher levels)
  • Exclusive cache hierarchies (data is present in only one level of the hierarchy)

Interconnects

• Interconnects are the communication channels that allow CPU cores, caches, and other components to exchange data and control signals
• The design of interconnects is critical for the performance and scalability of multi-core CPU architectures, as they can impact memory access latency and bandwidth
• Examples of interconnect technologies include:
  • Bus-based interconnects (Front-Side Bus)
  • Point-to-point interconnects (Intel QuickPath Interconnect, AMD HyperTransport)

GPU architectures

• GPU architectures are designed for massively parallel processing, making them well-suited for data-parallel workloads commonly found in scientific computing, machine learning, and computer graphics
• GPUs have become increasingly important in Exascale Computing systems, as they offer high performance and energy efficiency for many computational tasks
• However, programming GPUs can be more challenging than CPUs due to their specialized architectures and programming models

Massively parallel processing

• GPUs are designed for massively parallel processing, with hundreds or thousands of simple processing cores that can execute many threads simultaneously
• This architecture allows GPUs to achieve high throughput for data-parallel workloads, where the same operation is applied to many data elements independently
• Examples of massively parallel GPU architectures include:
  • NVIDIA Ampere (up to 6912 CUDA cores)
  • AMD CDNA 2 (up to 7680 stream processors)

SIMT execution model

• GPUs employ a Single Instruction, Multiple Thread (SIMT) execution model, where a single instruction is executed by multiple threads in parallel
• The SIMT model allows GPUs to efficiently process data-parallel workloads by exploiting the inherent parallelism in the application
• Examples of SIMT programming models include:
  • NVIDIA CUDA (Compute Unified Device Architecture)
  • OpenCL (Open Computing Language)

Streaming multiprocessors

• GPUs are organized into streaming multiprocessors (SMs), which are the basic building blocks of the GPU architecture
• Each SM contains multiple processing cores, shared memory, and cache, allowing for efficient execution of parallel threads
• Examples of streaming multiprocessor designs include:
  • NVIDIA Streaming Multiprocessor (SM)
  • AMD Compute Unit (CU)

Memory subsystems

• GPU memory subsystems are designed to provide high bandwidth and low latency access to data for the massively parallel processing cores
• GPUs typically employ a hierarchical memory structure, with global memory, shared memory, and cache levels optimized for different access patterns
• Examples of GPU memory subsystems include:
  • NVIDIA HBM2 (High Bandwidth Memory)
  • AMD GDDR6 (Graphics Double Data Rate)

Compute vs graphics workloads

• While GPUs were originally designed for graphics workloads, they have evolved to handle general-purpose compute workloads as well
• Compute workloads often have different requirements than graphics workloads, such as higher precision arithmetic and more complex memory access patterns
• Examples of compute-focused GPU architectures include:
  • NVIDIA Tesla (dedicated compute GPUs)
  • AMD Instinct (accelerators for HPC and AI)

Accelerator architectures

• Accelerator architectures are specialized processors designed to accelerate specific types of workloads, such as machine learning, signal processing, or cryptography
• These architectures offer significant performance and energy efficiency gains for targeted applications, making them attractive for Exascale Computing systems
• However, the specialized nature of accelerators can limit their flexibility and require significant effort to port existing applications

Domain-specific designs

• Accelerators are often designed for specific domains, such as machine learning or signal processing, allowing them to be optimized for the unique requirements of those workloads
• Domain-specific designs can offer significant performance and energy efficiency gains compared to general-purpose processors
• Examples of domain-specific accelerators include:
  • Google Tensor Processing Unit (TPU) for machine learning
  • Intel Movidius VPU (Vision Processing Unit) for computer vision

FPGAs for acceleration

• Field-Programmable Gate Arrays (FPGAs) are reconfigurable devices that can be programmed to implement custom hardware accelerators
• FPGAs offer flexibility and adaptability, allowing them to be optimized for specific workloads and updated as requirements change
• Examples of FPGA-based accelerators include:
  • Xilinx Alveo (adaptable accelerator cards)
  • Intel Stratix 10 (high-performance FPGAs)

ASICs for acceleration

• Application-Specific Integrated Circuits (ASICs) are custom-designed chips that are optimized for a specific task or application
• ASICs offer the highest performance and energy efficiency for the targeted workload but are less flexible and more expensive to develop than other accelerator options
• Examples of ASIC-based accelerators include:
  • Bitcoin mining ASICs (Bitmain Antminer, Canaan AvalonMiner)
  • Google TPU (Tensor Processing Unit) for machine learning

Coupling with host processors

• Accelerators are typically coupled with host processors (CPUs) to form a heterogeneous computing system, where the CPU manages overall system resources and offloads specific tasks to the accelerator
• The choice of coupling method (e.g., PCIe, NVLink, CXL) can impact the performance and efficiency of the overall system
• Examples of accelerator coupling technologies include:
  • PCIe (Peripheral Component Interconnect Express)
  • NVIDIA NVLink (high-bandwidth interconnect for GPUs)

Memory considerations

• Accelerators often have their own dedicated memory subsystems, which can have different capacity, bandwidth, and latency characteristics than the host processor's memory
• Efficient data movement between the host and accelerator memories is crucial for overall system performance and requires careful consideration of data layouts and transfer methods
• Examples of accelerator memory technologies include:
  • HBM (High Bandwidth Memory) for GPUs and FPGAs
  • GDDR6 (Graphics Double Data Rate) for GPUs

Heterogeneous computing

• Heterogeneous computing refers to the use of multiple types of processors (e.g., CPUs, GPUs, accelerators) within a single computing system to leverage their unique strengths and capabilities
• Heterogeneous architectures are becoming increasingly common in Exascale Computing systems, as they offer the potential for improved performance, energy efficiency, and adaptability to diverse workloads
• However, heterogeneous computing also introduces challenges in programming, resource management, and system integration

CPU-GPU collaboration

• CPU-GPU collaboration involves the cooperative use of CPUs and GPUs to accelerate applications, with the CPU handling control flow and serial tasks while the GPU handles data-parallel tasks
• Effective CPU-GPU collaboration requires careful partitioning of the workload, efficient data transfer between the processors, and synchronization of their execution
• Examples of CPU-GPU collaboration frameworks include:
  • NVIDIA CUDA Streams (concurrent execution of CPU and GPU tasks)
  • OpenCL (open standard for heterogeneous computing)

Accelerator integration

• Accelerator integration refers to the process of incorporating specialized accelerators (e.g., FPGAs, ASICs) into a computing system and enabling their use by applications
• Effective accelerator integration requires well-defined interfaces, drivers, and programming models that allow applications to leverage the accelerator's capabilities
• Examples of accelerator integration technologies include:
  • Intel OneAPI (unified programming model for heterogeneous computing)
  • Xilinx Vitis (development platform for FPGA-based accelerators)

Unified memory architectures

• Unified memory architectures provide a single, coherent memory space that is accessible by all processors in a heterogeneous system, simplifying programming and reducing the need for explicit data transfers
• Unified memory can be implemented through hardware support (e.g., shared physical memory) or software techniques (e.g., virtual memory, memory mapping)
• Examples of unified memory technologies include:
  • NVIDIA Unified Memory (single memory space for CPUs and GPUs)
  • Heterogeneous System Architecture (HSA) (industry standard for heterogeneous computing)

Programming models

• Programming models for heterogeneous computing provide abstractions and tools that allow developers to express parallelism and leverage the capabilities of different processors
• Effective programming models should balance performance, portability, and productivity, while providing mechanisms for data movement, synchronization, and resource management
• Examples of programming models for heterogeneous computing include:
  • NVIDIA CUDA (Compute Unified Device Architecture)
  • OpenMP (Open Multi-Processing)

Performance considerations

• Achieving high performance in Exascale Computing systems requires careful consideration of various factors, such as the balance between compute and memory capabilities, the trade-offs between latency and throughput, and the scalability of the system
• Performance optimization for heterogeneous architectures can be challenging, as it requires an understanding of the strengths and weaknesses of each processor type and the ability to map workloads effectively to the appropriate resources
• Key performance metrics for Exascale Computing systems include floating-point operations per second (FLOPS), memory bandwidth, and power efficiency

Compute-bound vs memory-bound

• Applications can be classified as either compute-bound or memory-bound, depending on whether their performance is limited by the available computational resources or memory bandwidth
• Compute-bound applications require high-performance processors with many cores and fast clock speeds, while memory-bound applications require high-bandwidth memory subsystems and efficient data movement
• Examples of compute-bound applications include:
  • Dense matrix multiplication
  • Fluid dynamics simulations

Latency vs throughput

• Latency and throughput are two key performance metrics for computing systems, with latency measuring the time required to complete a single task and throughput measuring the number of tasks completed per unit time
• Different processors and architectures may prioritize latency or throughput, depending on their design and the target workloads
• Examples of latency-sensitive applications include:
  • Online transaction processing (OLTP)
  • Real-time control systems

Scalability challenges

• Scalability refers to the ability of a computing system to maintain performance as the problem size or number of processors increases
• Exascale Computing systems face significant scalability challenges, such as managing communication and synchronization overhead, load balancing, and fault tolerance
• Examples of scalability bottlenecks include:
  • Communication latency and bandwidth limitations
  • Amdahl's Law (diminishing returns from parallelization)

Power efficiency

• Power efficiency is a critical concern for Exascale Computing systems, as the energy costs of operating large-scale systems can be substantial
• Improving power efficiency requires a combination of hardware and software techniques, such as dynamic voltage and frequency scaling (DVFS), power-aware scheduling, and energy-efficient algorithms
• Examples of power-efficient processor architectures include:
  • ARM big.LITTLE (heterogeneous multi-core architecture)
  • Intel Lakefield (hybrid CPU architecture)

Future architectural trends

• As Exascale Computing systems continue to evolve, new architectural trends and technologies are emerging to address the challenges of performance, power efficiency, and programmability
• These trends include the integration of novel computing paradigms, such as near-memory computing and neuromorphic computing, as well as the exploration of new materials and devices, such as quantum computing
• The adoption of these technologies will require significant research and development efforts, as well as the evolution of programming models and system software to support their integration

Near-memory computing

• Near-memory computing (NMC) is an architectural approach that aims to reduce the performance and energy costs of data movement by placing computation closer to memory
• NMC can be implemented through various techniques, such as processing-in-memory (PIM), where computation is performed directly in memory devices, or through the use of 3D-stacked memory with integrated logic
• Examples of near-memory computing technologies include:
  • Hybrid Memory Cube (HMC)
  • High Bandwidth Memory (HBM) with in-memory processing

Neuromorphic computing

• Neuromorphic computing is an approach that seeks to emulate the structure and function of biological neural networks in hardware, with the goal of achieving high energy efficiency and adaptability
• Neuromorphic processors typically consist of large arrays of simple, interconnected processing elements that communicate through spikes, similar to neurons in the brain
• Examples of neuromorphic computing platforms include:
  • Intel Loihi (research chip for spiking neural networks)
  • IBM TrueNorth (brain-inspired computing architecture)

Quantum computing potential

• Quantum computing is an emerging paradigm that harnesses the principles of quantum mechanics to perform certain computations much faster than classical computers
• While still in the early stages of development, quantum computers have the potential to accelerate certain tasks relevant to Exascale Computing, such as optimization, machine learning, and molecular simulations
• Examples of quantum computing technologies include:
  • Superconducting qubits (Google, IBM, Rigetti)
  • Trapped ion qubits (IonQ, Honeywell)