Heterogeneous Parallel Programming -1.2 Introduction to Heterogeneous Parallel Computing

Summary

This lecture introduces heterogeneous parallel computing, emphasizing the complementary roles of CPUs and GPUs. CPUs, designed for low-latency, sequential tasks, feature large caches, powerful ALUs, and sophisticated control for branch prediction and data forwarding. GPUs, optimized for high-throughput parallel tasks, have many energy-efficient ALUs, smaller caches, and simpler control. Modern mobile SoCs and supercomputers combine these cores with specialized IP blocks and on-chip memory to maximize performance. The lecture highlights how applications achieve optimal performance by leveraging both CPU and GPU strengths, covering domains like scientific simulation, data analysis, medical imaging, and digital media processing.

Takeaways

• 😀 Heterogeneous parallel computing combines latency-oriented devices (CPUs) and throughput-oriented devices (GPUs) to optimize performance.
• 😀 Modern mobile SoCs typically include 2–4 CPU cores, 2–4 GPU cores, DSPs, programmable IP blocks, and on-chip memory to manage workload efficiently.
• 😀 CPUs are designed for low-latency execution with features like large caches, sophisticated control logic, branch prediction, and data forwarding.
• 😀 GPUs are designed for high-throughput execution, featuring many lightweight ALUs, simple control logic, small caches, and support for massive threading.
• 😀 CPU ALUs perform arithmetic operations in very few clock cycles at high frequencies, minimizing latency for sequential tasks.
• 😀 GPU ALUs are heavily pipelined and power-efficient, capable of handling many operations in parallel, but each operation takes longer than on CPUs.
• 😀 Effective applications use CPUs for sequential sections and GPUs for parallel sections to maximize overall performance.
• 😀 In practice, GPUs can achieve 10× or more speedup for parallel sections, while CPUs achieve similar speedups for sequential parts.
• 😀 Heterogeneous parallel computing is widely used across multiple domains, including scientific simulations, financial analysis, medical imaging, digital media processing, computer vision, and interactive physics.
• 😀 Understanding the design philosophies and complementary strengths of CPUs and GPUs is foundational for successful programming of heterogeneous computing systems.

Q & A

What is the main objective of this lecture on heterogeneous parallel computing?

The main objective is to understand the major differences between latency-oriented devices (CPUs) and throughput-oriented devices (GPUs), and how modern applications increasingly use both types of devices.

What are latency devices and throughput devices in the context of this lecture?

Latency devices refer to CPU cores, which are optimized for low-latency operations. Throughput devices refer to GPU cores, which are optimized for high-throughput parallel execution.

How do modern mobile phones illustrate heterogeneous computing?

Modern mobile phones typically have 2–4 CPU cores for latency-sensitive tasks, 2–4 GPU cores for throughput tasks, DSP cores for specialized computation, and increasingly programmable IP blocks and configurable logic cores, all integrated with on-chip memory to reduce DRAM bandwidth usage.

What is the main difference between CPU and GPU cache usage?

CPUs have large caches designed to keep frequently accessed data close to execution units to reduce latency, whereas GPUs have smaller caches mainly used to consolidate memory requests from many threads, reducing DRAM traffic but not latency.

Why do CPUs have sophisticated control logic?

CPUs have sophisticated control logic to implement branch prediction, data forwarding, and pipeline management, which minimizes latency for sequential instructions and ensures efficient execution of latency-sensitive tasks.

How are GPU ALUs different from CPU ALUs?

GPU ALUs are energy-efficient, heavily pipelined, and designed for long-latency operations but high throughput, allowing many threads to execute in parallel. CPU ALUs are powerful, low-latency, and optimized for sequential arithmetic operations.

Why do modern applications use both CPUs and GPUs?

CPUs are used for sequential parts of an application where low latency is critical, while GPUs are used for parallel sections to leverage their high throughput. This combination maximizes overall performance.

What are some application domains that benefit from heterogeneous parallel computing?

Domains include financial analysis, scientific and engineering simulations, data-intensive analytics, medical imaging, digital audio/video processing, computer vision, biomedical informatics, electronic design automation, statistical modeling, numerical methods, rendering, and interactive physics.

How does branch prediction in CPUs help reduce latency?

Branch prediction allows the CPU to guess the likely path of conditional statements or loops, fetch and execute instructions along that path in advance, and thus reduce the delay caused by decision-making in sequential code execution.

What is the emerging trend in SOCs for making them more versatile?

The emerging trend is the use of configurable logic or configurable cores, which allows SOCs to adapt to various computing needs beyond fixed-function hardware blocks.

What is the difference in thread management between CPUs and GPUs?

CPUs support a smaller number of threads with complex control to optimize sequential execution, while GPUs support a very large number of threads with simpler control, enabling high parallel throughput.

What reading material is recommended for further understanding of CPU vs GPU architectures?

Chapter 1 of the textbook by David Kirk is recommended for deeper insights into CPU vs GPU design and heterogeneous parallel computing concepts.