UC Riverside

As parallel systems become ubiquitous, exploiting parallelism becomes crucial

for improving application performance. However, the complexities of developing

parallel software are major challenges. Shared memory parallel programming

models, such as OpenMP and Thread Building Blocks (TBBs), offer a single view

of the memory thereby making parallel programming easier. However, they

support limited forms of parallelism. Distributed memory programming models,

such as the Message Passing Interface (MPI), support more parallelism types;

however, their low level interfaces require great deal of programming effort.

This dissertation presents the SpiceC system that simplifies the task of

parallel programming while supporting different forms of parallelism and

parallel computing platforms. SpiceC provides easy to use directives to

express different forms of parallelism, including DOALL, DOACROSS, and

pipelining parallelism. SpiceC is based upon an intuitive computation model

in which each thread performs its computation in isolation from other

threads using its private space and communicates with other threads

via the shared space. Since, all data transfers between shared and

private spaces are explicit, SpiceC naturally supports both shared and

distributed memory platforms with ease.

SpiceC is designed to handle the complexities of real world applications.

The effectiveness of SpiceC is demonstrated both in terms of delivered

performance and the ease of parallelization for applications with the

following characteristics.

Applications that cannot be statically parallelized due to presence of

dependences, often contain large amounts of input dependent and dynamic

data level parallelism. SpiceC supports speculative parallelization

for exploiting dynamic parallelism with minimal programming effort.

Applications that operate on large data sets often make extensive use of

pointer-based dynamic data structures. SpiceC provides support for

partitioning dynamic data structures across threads and then distributing

the computation among the threads in a partition sensitive fashion.

Finally, due to large input sizes, many applications repeatedly perform

I/O operations that are interspersed with the computation. While traditional

approach is to execute loops contain I/O operations serially, SpiceC

introduces support for parallelizing computations in the presence

of I/O operations.

Finally, this dissertation demonstrates that SpiceC can handle the challenges

posed by the memory architectures of modern parallel computing platforms.

The memory architecture impacts the manner in which data transfers between

private and shared spaces are implemented. SpiceC does not place the

the burden of data transfers on the programmer. Therefore portability of SpiceC

to different platforms is achieved by simply modifying the handling of

data transfers by the SpiceC compiler and runtime. First, it is

shown how SpiceC can be targeted to shared memory architectures both with and

without hardware support for cache coherence. Next it is shown how accelerators

such as GPUs present in heterogeneous systems are exploited by SpiceC. Finally,

the ability of SpiceC to exploit the scalability of a distributed-memory system,

consisting of a cluster of multicore machines, is demonstrated.