Abstract
II
ABSTRACT
With the computing power and programmability of graphics processor uint (GPU)
increasing continuously, general purpose computing on GPU (GPGPU) is gradually
becoming a research hotspot. Usually the computing with GPGPU utilizes a
heterogeneous mode of CPU and GPU. Although the heterogeneous system based on
CPU-GPU can achieve good performance gains, program development and
performance optimization of it are more complexity compared with the homogeneous
system.
Computing on the heterogeneous system based on CPU-GPU will encounter a lot
of performance bottlenecks, such as load balancing, synchronization and delay, data
locality, task division and so on. These factors are essential to improve the
performance of the program. On the other hand, although the programming difficulty
of the heterogeneous system based on CPU-GPU reduced greatly due to the CUDA
programming model, the development requirement is still high for most of the serial
program developers. And when the underlying hardware changes, software developers
have to learn a new programming model and rewrite programs for the new hardware
platform, which increases the burden on the programmer. So it is very significate to
designing a simple and platform-independent multicore parallel programming model.
We mainly did the following researches:
(1) Analyzed the key factors which affect the performance of CUDA programs,
summarized the existing optimization methods comprehensively and proposed our
new optimization methods and optimization strategy, such as using atomic functions
to achieve synchronization between different thread blocks. For each optimization
method, we did experiments to verify its effectiveness and theoretical analysis. And
our method that exploiting atomic functions to synchronize different thread blocks is
4~5 times faster than existing method that restarting the kernel function.
(2) To further validate the effectiveness of various optimization methods, and
also to descripte the development process (algorithm design, programming,
performance optimization) of heterogeneous platform based on CPU-GPU, we
exploited CPU-GPU heterogeneous computing platform to solve the problem of DNA
or protein sequence alignment which is a bioinformatics problem, namely designed
and implemented a new column-based parallel Smith-Waterman algorithm based on
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