Alejandro Villegas

Hetero-mark, a benchmark suite for CPU-GPU collaborative computing

Graphics Processing Units (GPUs) can easily outperform CPUs in processing large-scale data parallel workloads, but are considered weak in processing serialized tasks and communicating with other devices. Pursuing a CPU-GPU collaborative computing model which takes advantage of both devices could provide an important breakthrough in realizing the full performance potential of heterogeneous computing. In recent years platform vendors and runtime systems have added new features such as unified memory space and dynamic parallelism, providing a path to CPU-GPU coordination and necessary programming infrastructure to support future heterogeneous applications. As the rate of adoption of CPU-GPU collaborative computing continues to increase, it becomes increasingly important to formalize CPU-GPU collaborative programming paradigms and understand the impact of this emerging model on overall application performance. We propose the Hetero-Mark to help heterogeneous system programmers understand CPU-GPU collaborative computing and to provide guidance to computer architects in order to enhance the design of the runtime and the driver. We summarize seven common CPU-GPU collaborative computing programming patterns and include at least one benchmark for each pattern in the suite. We also characterize different workloads in Hetero- Mark to analyze execution metrics specific to CPU-GPU collaborative computing, including CPU and GPU performance, CPU-GPU communication latency and memory transfer latency.

A Fast GIS-tool to Compute the Maximum Solar Energy on Very Large Terrains

This study presents a new functionality of Geographic Information Systems (GIS) to assess solar energy input on vast high resolution Digital Elevation Models (DEMs). This tool is able to find out 1) the maximum solar energy that can be captured on a surface situated at a determined hight on each point of the DEM and 2) the optimal angles (i.e., slope and orientation) that allow capturing this maximum energy. Insolator: the open source high performance solar radiation model, we developed in a previous work, is used as baseline for this tool. Contrarily to most existent GIS tools, the proposed algorithm doesn’t suffer memory limitations and is specially suitable for GPU-CPU heterogeneous systems. The experimental results show that the proposed algorithm is able to compute the maximum irradiation and optimal angles maps on large DEMs with high accuracy in very short times and showing a high scalability.

Hardware Support for Scratchpad Memory Transactions on GPU Architectures.

Graphics Processing Units (GPUs) have become the accelerator of choice for data-parallel applications, enabling the execution of thousands of threads in a Single Instruction - Multiple Thread (SIMT) fashion. Using OpenCL terminology, GPUs offer a global memory space shared by all the threads in the GPU, as well as a low-latency local memory space shared by a subset of the threads. The latter is used as a scratchpad to improve the performance of the applications.

Toward a software transactional memory for heterogeneous CPU–GPU processors

The heterogeneous accelerated processing units (APUs) integrate a multi-core CPU and a GPU within the same chip. Modern APUs implement CPU–GPU platform atomics for simple data types. However, ensuring atomicity for complex data types is a task delegated to programmers. Transactional memory (TM) is an optimistic approach to achieve this goal. With TM, shared data can be accessed by multiple computing threads speculatively, but changes are only visible if a transaction ends with no conflict with others in its memory accesses. In this paper we present APUTM, a software TM designed for APU processors which focuses on minimizing the access to shared metadata. The main goal of APUTM is to understand the trade-offs of implementing a software TM on such platform. In our experiments, APUTM is able to outperform sequential execution of the applications. Additionally, we compare its adaptability to execute in one of the devices or in both simultaneously.