Object-oriented programming is often regarded as too inefficient for high-performance computing (HPC), despite the fact that many important HPC problems have an inherent object structure. Our goal is to bring efficient, object-oriented programming to massively parallel SIMD architectures, especially GPUs.
In this thesis, we develop various techniques for optimizing object-oriented GPU code. Most notably, we identify the object-oriented Single-Method Multiple-Objects (SMMO) programming model. We first develop an embedded C++ Structure of Arrays (SOA) data layout DSL for SMMO applications. We then design a lock-free, dynamic memory allocator that stores allocations in SOA layout. Finally, we show how to further optimize the memory access of SMMO applications with memory defragmentation.
Constraint-logic object-oriented programming provides a useful symbiosis between object-oriented programming and constraint-logic search. The ability to use logic variables, constraints, non-deterministic search, and object-oriented programming in an integrated way facilitates the combination of search-related program parts and other business logic in object-oriented applications. With this work we conceptualize array-typed logic variables ("free arrays"), thus completing the set of types that logic variables can assume in constraint-logic object-oriented programming. Free arrays exhibit inter…
The SIMT execution model is commonly used for general GPU development. CUDA and OpenCL developers write scalar code that is implicitly parallelized by compiler and hardware. On Intel GPUs, however, this abstraction has profound performance implications as the underlying ISA is SIMD and important hardware capabilities cannot be fully utilized. To close this performance gap we introduce C-For-Metal (CM), an explicit SIMD programming framework designed to deliver close-to-the-metal performance on Intel GPUs. The CM programming language and its vector/matrix types provide an intuitive interface to…
As GPU availability has increased and programming support has matured, a wider variety of applications are being ported to these platforms. Many parallel applications contain fine-grained synchronization idioms; as such, their correct execution depends on a degree of relative forward progress between threads (or thread groups). Unfortunately, many GPU programming specifications say almost nothing about relative forward progress guarantees between workgroups. Although prior work has proposed a spectrum of plausible progress models for GPUs, cross-vendor specifications have yet to commit to any …
Since the advent of LISP, the fifth generation programming language has developed for decades. However, compared with the fourth generation programming language, the fifth generation programming language has not been widely used because of its obscure semantics, rigorous representation of problems, and limited inference ability. For this reason, COOL (Constraint and Object Ordered Language), a fifth generation programming language proposed in this paper, overcomes the problems of intuitive semantics, rigorous restrictions on handling problem conditions, and improves the inference ability of pr…
Writing parallel codes is difficult and exhibits a fundamental trade-off between abstraction and performance. The high level language abstractions designed to simplify the complexities of parallelism make certain assumptions that impacts performance and scalability. On the other hand lower level languages, providing many opportunities for optimisation, require in-depth knowledge and the programmer to consider tricky details of parallelism. An approach is required which can bridge the gap and provide both the ease of programming and opportunities for control and optimisation. By optionally deco…
Interdisciplinary Centre for Security, Reliability and Trust (SnT)（安全、可靠性与信任跨学科中心（SnT））；University of Luxembourg（卢森堡大学）
The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed of processors stagnates. Unfortunately, constraint programming solvers do not take advantage yet of GPU parallelism. One reason is that constraint solvers were primarily designed within the mental frame of sequential computation. To solve this issue, we take a step back and contribute to a simple, intrinsically parallel, lock-free and formally correct programming language based on concurrent constraint programming. We then re-examine parallel constraint solving on GPUs within this fo…
The Partitioned Global Address Space memory model has been popularised by a number of languages and applications. However this abstraction can often result in the programmer having to rely on some in built choices and with this implicit parallelism, with little assistance by the programmer, the scalability and performance of the code heavily depends on the compiler and choice of application. We propose an approach, type oriented programming, where all aspects of parallelism are encoded via types and the type system. The type information associated by the programmer will determine, for instance…
