VIPER : a 25-MHz, 100-MIPS peak VLIW micro-processor

This paper describes the design and implementation of a very long instruction word (VLIW) microprocessor. The VIPER (VLIW integer processor) contains four pipelined functional units, and can achieve 100 MIPS peak performance at 25 MHz. The procesor is capable of performing multiway branch operations, two load/store operations and up to four ALU operations in each clock cycle, with full register file access to each functional unit. VIPER is the first VLIW microprocessor known that can achieve this level of performance. Designed in twelve months, the processor is integrated with an instruction cache controller and a data cache, requiring 450,000 transistors and a die size of 12.9 by 9.1 mm in a 1.2 µm technology

Memory Subsystem Optimization Techniques for Modern High-Performance General-Purpose Processors

abstract: General-purpose processors propel the advances and innovations that are the subject of humanity’s many endeavors. Catering to this demand, chip-multiprocessors (CMPs) and general-purpose graphics processing units (GPGPUs) have seen many high-performance innovations in their architectures. With these advances, the memory subsystem has become the performance- and energy-limiting aspect of CMPs and GPGPUs alike. This dissertation identifies and mitigates the key performance and energy-efficiency bottlenecks in the memory subsystem of general-purpose processors via novel, practical, microarchitecture and system-architecture solutions. Addressing the important Last Level Cache (LLC) management problem in CMPs, I observe that LLC management decisions made in isolation, as in prior proposals, often lead to sub-optimal system performance. I demonstrate that in order to maximize system performance, it is essential to manage the LLCs while being cognizant of its interaction with the system main memory. I propose ReMAP, which reduces the net memory access cost by evicting cache lines that either have no reuse, or have low memory access cost. ReMAP improves the performance of the CMP system by as much as 13%, and by an average of 6.5%. Rather than the LLC, the L1 data cache has a pronounced impact on GPGPU performance by acting as the bandwidth filter for the rest of the memory subsystem. Prior work has shown that the severely constrained data cache capacity in GPGPUs leads to sub-optimal performance. In this thesis, I propose two novel techniques that address the GPGPU data cache capacity problem. I propose ID-Cache that performs effective cache bypassing and cache line size selection to improve cache capacity utilization. Next, I propose LATTE-CC that considers the GPU’s latency tolerance feature and adaptively compresses the data stored in the data cache, thereby increasing its effective capacity. ID-Cache and LATTE-CC are shown to achieve 71% and 19.2% speedup, respectively, over a wide variety of GPGPU applications. Complementing the aforementioned microarchitecture techniques, I identify the need for system architecture innovations to sustain performance scalability of GPG- PUs in the face of slowing Moore’s Law. I propose a novel GPU architecture called the Multi-Chip-Module GPU (MCM-GPU) that integrates multiple GPU modules to form a single logical GPU. With intelligent memory subsystem optimizations tailored for MCM-GPUs, it can achieve within 7% of the performance of a similar but hypothetical monolithic die GPU. Taking a step further, I present an in-depth study of the energy-efficiency characteristics of future MCM-GPUs. I demonstrate that the inherent non-uniform memory access side-effects form the key energy-efficiency bottleneck in the future. In summary, this thesis offers key insights into the performance and energy-efficiency bottlenecks in CMPs and GPGPUs, which can guide future architects towards developing high-performance and energy-efficient general-purpose processors.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Hardware schemes for early register release

Register files are becoming one of the critical components of current out-of-order processors in terms of delay and power consumption, since their potential to exploit instruction-level parallelism is quite related to the size and number of ports of the register file. In conventional register renaming schemes, register releasing is conservatively done only after the instruction that redefines the same register is committed. Instead, we propose a scheme that releases registers as soon as the processor knows that there will be no further use of them. We present two early releasing hardware implementations with different performance/complexity trade-offs. Detailed cycle-level simulations show either a significant speedup for a given register file size, or a reduction in register file size for a given performance level.Peer ReviewedPostprint (published version

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

Orchestrating thread scheduling and cache management to improve memory system throughput in throughput processors

textThroughput processors such as GPUs continue to provide higher peak arithmetic capability. Designing a high throughput memory system to keep the computational units busy is very challenging. Future throughput processors must continue to exploit data locality and utilize the on-chip and off-chip resources in the memory system more effectively to further improve the memory system throughput. This dissertation advocates orchestrating the thread scheduler with the cache management algorithms to alleviate GPU cache thrashing and pollution, avoid bandwidth saturation and maximize GPU memory system throughput. Based on this principle, this thesis work proposes three mechanisms to improve the cache efficiency and the memory throughput. This thesis work enhances the thread throttling mechanism with the Priority-based Cache Allocation mechanism (PCAL). By estimating the cache miss ratio with a variable number of cache-feeding threads and monitoring the usage of key memory system resources, PCAL determines the number of threads to share the cache and the minimum number of threads bypassing the cache that saturate memory system resources. This approach reduces the cache thrashing problem and effectively employs chip resources that would otherwise go unused by a pure thread throttling approach. We observe 67% improvement over the original as-is benchmarks and a 18% improvement over a better-tuned warp-throttling baseline. This work proposes the AgeLRU and Dynamic-AgeLRU mechanisms to address the inter-thread cache thrashing problem. AgeLRU prioritizes cache blocks based on the scheduling priority of their fetching warp at replacement. Dynamic-AgeLRU selects the AgeLRU algorithm and the LRU algorithm adaptively to avoid degrading the performance of non-thrashing applications. There are three variants of the AgeLRU algorithm: (1) replacement-only, (2) bypassing, and (3) bypassing with traffic optimization. Compared to the LRU algorithm, the above mentioned three variants of the AgeLRU algorithm enable increases in performance of 4%, 8% and 28% respectively across a set of cache-sensitive benchmarks. This thesis work develops the Reuse-Prediction-based cache Replacement scheme (RPR) for the GPU L1 data cache to address the intra-thread cache pollution problem. By combining the GPU thread scheduling priority together with the fetching Program Counter (PC) to generate a signature as the index of the prediction table, RPR identifies and prioritizes the near-reuse blocks and high-reuse blocks to maximize the cache efficiency. Compared to the AgeLRU algorithm, the experimental results show that the RPR algorithm results in a throughput improvement of 5% on average for regular applications, and a speedup of 3.2% on average across a set of cache-sensitive benchmarks. The techniques proposed in this dissertation are able to alleviate the cache thrashing, cache pollution and resource saturation problems effectively. We believe when these techniques are combined, they will synergistically further improve GPU cache efficiency and the overall memory system throughput.Computer Science

Improving Energy Efficiency of Application-Specific Instruction-Set Processors

Present-day consumer mobile devices seem to challenge the concept of embedded computing by bringing the equivalent of supercomputing power from two decades ago into hand-held devices. This challenge, however, is well met by pushing the boundaries of embedded computing further into areas previously monopolised by Application-Specific Integrated Circuits (ASICs). Furthermore, in areas traditionally associated with embedded computing, an increase in the complexity of algorithms and applications requires a continuous rise in availability of computing power and energy efficiency in order to fit within the same, or smaller, power budget. It is, ultimately, the amount of energy the application execution consumes that dictates the usefulness of a programmable embedded system, in comparison with implementation of an ASIC.This Thesis aimed to explore the energy efficiency overheads of Application-Specific InstructionSet Processors (ASIPs), a class of embedded processors aiming to compete with ASICs. While an ASIC can be designed to provide precise performance and energy efficiency required by a specific application without unnecessary overheads, the cost of design and verification, as well as the inability to upgrade or modify, favour more flexible programmable solutions. The ASIP designs can match the computing performance of the ASIC for specific applications. What is left, therefore, is achieving energy efficiency of a similar order of magnitude.In the past, one area of ASIP design that has been identified as a major consumer of energy is storage of temporal values produced during computation – the Register File (RF), with the associated interconnection network to transport those values between registers and computational Function Units (FUs). In this Thesis, the energy efficiency of RF and interconnection network is studied using the Transport Triggered Architectures (TTAs) template. Specifically, compiler optimisations aiming at reducing the traffic of temporal values between RF and FUs are presented in this Thesis. Bypassing of the temporal value, from the output of the FU which produces it directly in the input ports of the FUs that require it to continue with the computation, saves multiple RF reads. In addition, if all the uses of such a temporal value can be bypassed, the RF write can be eliminated as well. Such optimisations result in a simplification of the RF, via a reduction in the actual number of registers present or a reduction in the number of read and write ports in the RF and improved energy efficiency. In cases where the limited number of the simultaneous RF reads or writes cause a performance bottleneck, such optimisations result in performance improvements leading to faster execution times, therefore, allowing for execution at lower clock frequencies resulting in additional energy savings.Another area of the ASIP design consuming a significant amount of energy is the instruction memory subsystem, which is the artefact required for the programmability of the embedded processor. As this subsystem is not present in ASIC, the energy consumed for storing an application program and reading it from the instruction memories to control processor execution is an overhead that needs to be minimised. In this Thesis, one particular tool to improve the energy efficiency of the instruction memory subsystem – instruction buffer – is examined. While not trivially obvious, the presence of buffers for storing loop bodies, or parts of them, results in a reduced number of reads from the instruction memories. As a result, memories can be put to lower power state leading to lower overall energy consumption, pending energy-efficient buffer implementation. Specifically, an energy-efficient implementation of the instruction buffer is presented in this Thesis, together with analysis tools to identify candidate loops and assess their suitability for storing in the instruction buffer.The studies presented in this Thesis show that the energy overheads associated with the use of embedded processors, in comparison to ad-hoc ASIC solutions, are manageable when carefully considered during the design of an embedded system for a particular application, or application domain. Finally, the methods presented in this Thesis do not restrict the reprogrammability of the embedded system