Reducing Cache Contention On GPUs

The usage of Graphics Processing Units (GPUs) as an application accelerator has become increasingly popular because, compared to traditional CPUs, they are more cost-effective, their highly parallel nature complements a CPU, and they are more energy efficient. With the popularity of GPUs, many GPU-based compute-intensive applications (a.k.a., GPGPUs) present significant performance improvement over traditional CPU-based implementations. Caches, which significantly improve CPU performance, are introduced to GPUs to further enhance application performance. However, the effect of caches is not significant for many cases in GPUs and even detrimental for some cases. The massive parallelism of the GPU execution model and the resulting memory accesses cause the GPU memory hierarchy to suffer from significant memory resource contention among threads. One cause of cache contention arises from column-strided memory access patterns that GPU applications commonly generate in many data-intensive applications. When such access patterns are mapped to hardware thread groups, they become memory-divergent instructions whose memory requests are not GPU hardware friendly, resulting in serialized access and performance degradation. Cache contention also arises from cache pollution caused by lines with low reuse. For the cache to be effective, a cached line must be reused before its eviction. Unfortunately, the streaming characteristic of GPGPU workloads and the massively parallel GPU execution model increase the reuse distance, or equivalently reduce reuse frequency of data. In a GPU, the pollution caused by a large reuse distance data is significant. Memory request stall is another contention factor. A stalled Load/Store (LDST) unit does not execute memory requests from any ready warps in the issue stage. This stall prevents the potential hit chances for the ready warps. This dissertation proposes three novel architectural modifications to reduce the contention: 1) contention-aware selective caching detects the memory-divergent instructions caused by the column-strided access patterns, calculates the contending cache sets and locality information and then selectively caches; 2) locality-aware selective caching dynamically calculates the reuse frequency with efficient hardware and caches based on the reuse frequency; and 3) memory request scheduling queues the memory requests from a warp issuing stage, frees the LDST unit stall and schedules items from the queue to the LDST unit by multiple probing of the cache. Through systematic experiments and comprehensive comparisons with existing state-of-the-art techniques, this dissertation demonstrates the effectiveness of our aforementioned techniques and the viability of reducing cache contention through architectural support. Finally, this dissertation suggests other promising opportunities for future research on GPU architecture

Computer Architectures to Close the Loop in Real-time Optimization

© 2015 IEEE.Many modern control, automation, signal processing and machine learning applications rely on solving a sequence of optimization problems, which are updated with measurements of a real system that evolves in time. The solutions of each of these optimization problems are then used to make decisions, which may be followed by changing some parameters of the physical system, thereby resulting in a feedback loop between the computing and the physical system. Real-time optimization is not the same as fast optimization, due to the fact that the computation is affected by an uncertain system that evolves in time. The suitability of a design should therefore not be judged from the optimality of a single optimization problem, but based on the evolution of the entire cyber-physical system. The algorithms and hardware used for solving a single optimization problem in the office might therefore be far from ideal when solving a sequence of real-time optimization problems. Instead of there being a single, optimal design, one has to trade-off a number of objectives, including performance, robustness, energy usage, size and cost. We therefore provide here a tutorial introduction to some of the questions and implementation issues that arise in real-time optimization applications. We will concentrate on some of the decisions that have to be made when designing the computing architecture and algorithm and argue that the choice of one informs the other

Optimizing sorting algorithms for the Cell Broadband Engine

The quest for higher performance in computationally intensive tasks is and will always be an ongoing effort. General purpose processors (GPP) have not been sufficient for many of these tasks which has led to research focused towards computing on specialty processors and graphics processing units (GPU). While GPU provide sufficient speedups for some tasks, other specialty processors may be better suited, more economical, or more efficient for different types of tasks. Sorting is an important task in many applications and can be computationally intensive when dealing with large data sets. One such specialty processor that has proven to be a viable solution for sorting is the Cell Broadband Engine (CBE). The CBE is being used as the main platform for this thesis since there are already applications for it that require sorting software. The Cell processor is a general purpose processor that combines one master PowerPC core with eight other vector processors connected via a high bandwidth interconnect bus. The user must explicitly manage the communication, scheduling, and load-balancing between the vector processors and the PowerPC processor to achieve the highest efficiency. By optimizing the sorting algorithms for the vector processors, large speedups can be achieved because multiple operations occur simultaneously. Optimized sorting software is often sought when sorting is not the main purpose of the application. This keeps overheads low so that the performance gains can be realized from the actual code that is to be optimized on specialty processors. Often having sorted datasets enable algorithms to run faster and are more predictably. The motivation behind this thesis is that there is currently no standard library of sorting algorithms that have been optimized for the CBE. Lack of standard libraries makes writing code for the CBE difficult. Results from previous works have also not been sufficient in providing specific measurements of sorting performance. This thesis will explore the development and analysis of a variety of optimized parallel sorting algorithms written for the Cell processor. This thesis will focus on the sorting of both individual elements within vectors as well as sorting entire vectors within arrays. The sorting algorithms, written in C++, that will be optimized and analyzed include, but are not limited to bitonic sort, heap sort, merge sort, and quick sort. A communication management framework will also be created as a main focus of this thesis in order to better understand the architecture of the processor

Study of OpenCL processing models for FPGA device

In our study, we present the results of the implementation of SHA-512 algorithm in FPGA. The distinguished element of our work is that we conducted the work using OpenCL for FPGA which is a relatively new development method for reconfigurable logic. We examine the loop unrolling; as the OpenCL performance optimisation method, and compare the efficiency of the different kernel implementation types: NDRange, Single-Work Item, and SIMD kernels. In conclusions, we compare metrics of the created FPGA accelerator to the corresponding GPGPU solutions. Also, our paper is accompanied by the source code repository to allow the reader to follow and extend our survey

Reconfigurable acceleration of Recurrent Neural Networks

Recurrent Neural Networks (RNNs) have been successful in a wide range of applications involving temporal sequences such as natural language processing, speech recognition and video analysis. However, RNNs often require a significant amount of memory and computational resources. In addition, the recurrent nature and data dependencies in RNN computations can lead to system stall, resulting in low throughput and high latency. This work describes novel parallel hardware architectures for accelerating RNN inference using Field-Programmable Gate Array (FPGA) technology, which considers the data dependencies and high computational costs of RNNs. The first contribution of this thesis is a latency-hiding architecture that utilizes column-wise matrix-vector multiplication instead of the conventional row-wise operation to eliminate data dependencies and improve the throughput of RNN inference designs. This architecture is further enhanced by a configurable checkerboard tiling strategy which allows large dimensions of weight matrices, while supporting element-based parallelism and vector-based parallelism. The presented reconfigurable RNN designs show significant speedup over CPU, GPU, and other FPGA designs. The second contribution of this thesis is a weight reuse approach for large RNN models with weights stored in off-chip memory, running with a batch size of one. A novel blocking-batching strategy is proposed to optimize the throughput of large RNN designs on FPGAs by reusing the RNN weights. Performance analysis is also introduced to enable FPGA designs to achieve the best trade-off between area, power consumption and performance. Promising power efficiency improvement has been achieved in addition to speeding up over CPU and GPU designs. The third contribution of this thesis is a low latency design for RNNs based on a partially-folded hardware architecture. It also introduces a technique that balances initiation interval of multi-layer RNN inferences to increase hardware efficiency and throughput while reducing latency. The approach is evaluated on a variety of applications, including gravitational wave detection and Bayesian RNN-based ECG anomaly detection. To facilitate the use of this approach, we open source an RNN template which enables the generation of low-latency FPGA designs with efficient resource utilization using high-level synthesis tools.Open Acces