Department of
Computer Science and
Engineering
UNIT 5 – HPC with CUDA
Subject Name : MODERN COMPUTER ARCHITECTURE
Course Code : 10211CS129
School of Computing
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of
Science and Technology
Unit-4::Syllabus
UNIT- V
Unit 5 HPC with CUD (HPC) 9Hours
CUDA programming model
Basic principles of CUDA programming
Concepts of threads and blocks,
GPU and CPU data exchange.
Unit-4:: GPU and CPU data exchange
Understanding the Divide
GPUs (Graphics Processing Units) and CPUs (Central Processing
Units) are designed for different tasks.
GPUs excel at parallel computations, making them ideal for
tasks like graphics rendering, scientific simulations, and
machine learning.
CPUs, on the other hand, are better suited for sequential tasks
and complex decision-making.
The Need for Collaboration
Many modern applications, especially in data-intensive fields
like machine learning and scientific computing, require both the
computational power of GPUs and the flexibility of CPUs. This
necessitates efficient data exchange between these two types of
processors.
Unit-4:: GPU and CPU data exchange
Data Exchange Mechanisms
1.Direct Memory Access (DMA):
1. How it works: The GPU can directly access and transfer data
from/to the system memory without involving the CPU.
2. Advantages: Efficient for large data transfers, reduces CPU
overhead.
3. Disadvantages: Requires careful memory management to avoid
conflicts.
2.CPU-GPU Copy:
1. How it works: The CPU explicitly copies data between its
memory and the GPU's memory.
2. Advantages: Simple to implement, provides flexibility in data
management.
3. Disadvantages: Can be less efficient for large data transfers,
especially if the CPU is busy.
Unit-4:: GPU and CPU data exchange
Data Exchange Mechanisms
3. Shared Memory:
1. How it works: Some GPUs have shared memory that can be
accessed by both the CPU and GPU.
2. Advantages: Extremely fast data exchange, reduces memory
traffic.
3. Disadvantages: Limited size, requires careful memory
management to avoid conflicts.
4. Unified Memory:
4. How it works: A unified memory space is presented to both the
CPU and GPU, allowing them to access the same memory
without explicit transfers.
5. Advantages: Simplifies programming, reduces overhead.
6. Disadvantages: Can introduce complexity in memory
management.
Unit-4:: GPU and CPU data exchange
Factors Affecting Data Exchange Performance
•Data Size: Larger data transfers benefit from DMA or
unified memory.
•Data Access Patterns: Sequential access is generally
more efficient than random access.
•GPU Architecture: Different GPU architectures have
varying capabilities and limitations for data exchange.
•Software Framework: The choice of programming
framework (e.g., CUDA, OpenCL) can significantly
impact performance.
Unit-4:: GPU and CPU data exchange
Optimizing Data Exchange
•Minimize Data Transfers: Use techniques like data compression,
caching, and efficient algorithms to reduce the amount of data
that needs to be transferred.
•Overlap Computation and Data Transfer: While the GPU is
processing data, the CPU can prepare the next batch, reducing
idle time.
•Choose the Right Mechanism: Select the data exchange
mechanism that best suits your application's needs based on
factors like data size, access patterns, and GPU architecture.
•Utilize Hardware Features: Leverage hardware-specific features
like DMA, shared memory, and unified memory to optimize
performance.
Department of
Computer Science and
Engineering
Thank You
School of Computing
Vel Tech Rangarajan Dr. Sagunthala R&D Institute of
Science and Technology
