This paper presents Automatic Algorithm Discoverer (AAD), an evolutionary framework for synthesizing programs of high complexity. To guide evolution, prior evolutionary algorithms have depended on fitness (objective) functions that are often challenging to design. To make evolutionary progress, instead, AAD employs Problem Guided Evolution (PGE), which requires introduction of a group of problems. Solutions discovered for simpler problems are used to solve more complex problems in the group. PGE also enables new evolutionary strategies. The above enable AAD to discover algorithms of similar or higher complexity relative to the state-of-the-art. Specifically, AAD produces Python code for 29 array/vector problems ranging from min, max, reverse, to more challenging problems like sorting and matrix-vector multiplication.

GLAF, short for Grid-based Language and Auto-parallelization Framework, is a programming framework that seeks to democratize parallel programming by facilitating better productivity in parallel computing via an intuitive graphical programming interface (GPI) that automatically parallelizes and generates code in many languages. Originally, GLAF addressed program development from scratch via the GPI; but this unduly restricted GLAF's utility to creating new codes only. Thus, this paper extends GLAF by enabling program development from pre-existing kernels of interest, which can then be easily and transparently integrated into existing legacy codes. Specifically, we address the theoretical and practical limitations of integration and interoperability of auto-generated parallel code within existing FORTRAN codes; enhance GLAF to overcome these limitations; and present an integrative case study and evaluation of the enhanced GLAF via the implementation of important kernels in two NASA codes: (1) the Synoptic Surface & Atmospheric Radiation Budget (SARB), part of the Clouds and the Earth's Radiant Energy System (CERES), and (2) the Fully Unstructured Navier-Stokes (FUN3D) suite for computational fluid dynamics.

Programming FPGAs has been an arduous task that requires extensive knowledge of hardware design languages (HDLs), such as Verilog or VHDL, and low-level hardware details. With OpenCL support for FPGAs, the design, prototyping and implementation of an FPGA is increasingly moving towards a much higher level of abstraction, when compared to the intrinsically low-level nature of HDLs. On the other hand, in the context of traditional (i.e., CPU) software development, OpenCL is still considered to be low-level and complex because the programmer needs to manually expose parallelism in the code. In this work, we present our approach to enhancing FPGA programmability via GLAF, a visual programming framework, to automatically generate synthesizable OpenCL code with an array of FPGA-specific optimizations. We find that our tool facilitates the development process and produces functionally correct and well-performing code on the FPGA for our molecular modeling, gene sequence search, and filtering algorithms.

The past decade's computing revolution has delivered parallel hardware to the masses. However, the ability to exploit its capabilities and ignite scientific breakthrough at a proportionate level remains a challenge due to the lack of parallel programming expertise. Although different solutions have been proposed to facilitate harvesting the seeds of parallel computing, most target seasoned programmers and ignore the special nature of a target audience like domain experts. This paper addresses the challenge of realizing a programming abstraction and implementing an integrated development framework for this audience. We present GLAF -- a grid-based language and auto-parallelizing, auto-tuning framework. Its key elements are its intuitive visual programming interface, which attempts to render expressing and validating an algorithm easier for domain experts, and its ability to automatically generate efficient serial and parallel Fortran and C code, including potentially beneficial code modifications (e.g., With respect to data layout). We find that the above features assist novice programmers to avoid common programming pitfalls and provide fast implementations.

The real-time execution of contemporary complex media applications requires energy-efficient processing capabilities beyond those of current superscalar processors. We observe that the complexity of contemporary media applications requires support for multiple forms of parallelism, including ILP, TLP, and various forms of DLP, such as subword SIMD, short vectors, and streams. Based on our observations, we propose an architecture, called ALP, that efficiently integrates all of these forms of parallelism with evolutionary changes to the programming model and hardware. The novel part of ALP is a DLP technique called SIMD vectors and streams (SVectors/SStreams) , which is integrated within a conventional superscalar-based CMP/SMT architecture with subword SIMD. This technique lies between subword SIMD and vectors, providing significant benefits over the former at a lower cost than the latter. Our evaluations show that each form of parallelism supported by ALP is important. Specifically, SVectors/SStreams are effective, compared to a system with the other enhancements in ALP. They give speedups of 1.1 to 3.4X and energy-delay product improvements of 1.1 to 5.1X for applications with DLP.

This paper compares the energy efficiency of chip multiprocessing (CMP) and simultaneous multithreading (SMT) on modern out-of-order processors for the increasingly important multimedia applications. Since performance is an important metric for real-time multimedia applications, we compare configurations at equal performance. We perform this comparison for a large number of performance points derived using different processor architectures and frequencies/voltages.We find that for the design space explored, for each workload, at each performance point, CMP is more energy efficient than SMT. The difference is small for two thread systems, but large (18% to 44%) for four thread systems. We also find that the best SMT and the best CMP configuration for a given performance target have different architecture and frequency/voltage. Therefore, their relative energy efficiency depends on a subtle interplay between various factors such as capacitance, voltage, IPC, frequency, and the level of clock gating, as well as workload features. We perform a detailed analysis considering these factors and develop a mathematical model to explain these results.Although CMP shows a clear energy advantage for four-thread (and higher) workloads, it comes at the cost of increased silicon area. We therefore investigate a hybrid solution where a CMP is built out of SMT cores, and find it to be an effective compromise. Finally, we find that we can reduce energy further for CMP with a straightforward application of previously proposed techniques of adaptive architectures and dynamic voltage/frequency scaling.

This work concerns algorithms to control energy-driven architecture adaptations for multimedia applications, without and with dynamic voltage scaling (DVS). We identify a broad design space for adaptation control algorithms based on two attributes: (1) when to adapt or temporal granularity and (2) what structures to adapt or spatial granularity. For each attribute, adaptation may be global or local. Our previous work developed a temporally and spatially global algorithm. It invokes adaptation at the granularity of a full frame of a multimedia application (temporally global) and considers the entire hardware configuration at a time (spatially global). It exploits inter-frame execution time variability, slowing computation just enough to eliminate idle time before the real-time deadline.This paper explores temporally and spatially local algorithms and their integration with the previous global algorithm. The local algorithms invoke architectural adaptation within an application frame to exploit intra-frame execution variability, and attempt to save energy without affecting execution time. We consider local algorithms previously studied for non-real-time applications as well as propose new algorithms. We find that, for systems without and with DVS, the local algorithms are effective in saving energy for multimedia applications, but the new integrated global and local algorithm is best for the systems and applications studied.