Clustered VLIW architecture based on queue register files

Institute for Computing Systems ArchitectureInstruction-level parallelism (ILP) is a set of hardware and software techniques that allow parallel execution of machine operations. Superscalar architectures rely most heavily upon hardware schemes to identify parallelism among operations. Although successful in terms of performance, the hardware complexity involved might limit the scalability of this model. VLIW architectures use a different approach to exploit ILP. In this case all data dependence analyses and scheduling of operations are performed at compile time, resulting in a simpler hardware organization. This allows the inclusion of a larger number of functional units (FUs) into a single chip. IN spite of this relative simplification, the scalability of VLIW architectures can be constrained by the size and number of ports of the register file. VLIW machines often use software pipelining techniques to improve the execution of loop structures, which can increase the register pressure. Furthermore, the access time of a register file can be compromised by the number of ports, causing a negative impact on the machine cycle time. For these reasons we understand that the benefits of having parallel FUs, which have motivated the investigation of alternative machine designs. This thesis presents a scalar VLIW architecture comprising clusters of FUs and private register files. Register files organised as queue structures are used as a mechanism for inter-cluster communication, allowing the enforcement of fixed latency in the process. This scheme presents better possibilities in terms of scalability as the size of the individual register files is not determined by the total number of FUs, suggesting that the silicon area may grow only linearly with respect to the total number of FUs. However, the effectiveness of such an organization depends on the efficiency of the code partitioning strategy. We have developed an algorithm for a clustered VLIW architecture integrating both software pipelining and code partitioning in a a single procedure. Experimental results show it may allow performance levels close to an unclustered machine without communication restraints. Finally, we have developed silicon area and cycle time models to quantify the scalability of performance and cost for this class of architecture

Memory Hierarchy Design for Next Generation Scalable Many-core Platforms

Performance and energy consumption in modern computing platforms is largely dominated by the memory hierarchy. The increasing computational power in the multiprocessors and accelerators, and the emergence of the data-intensive workloads (e.g. large-scale graph traversal and scientific algorithms) requiring fast transfer of large volumes of data, are two main trends which intensify this problem by putting even higher pressure on the memory hierarchy. This increasing gap between computation speed and data transfer speed is commonly referred as the “memory wall” problem. With the emergence of heterogeneous Three Dimensional (3D) Integration based on through-silicon-vias (TSV), this situation has started to recover in the past years. On one hand, it is now possible to improve memory access bandwidth and/or latency by either stacking memories directly on top of processors or through abstracted memory interfaces such as Micron’s Hybrid Memory Cube (HMC). On the other hand, near memory computation has become worthy of revisiting due to the cost-effective integration of logic and memory in 3D stacks. These two directions bring about several interesting opportunities including performance improvement, energy and cost reduction, product miniaturization, and modular design for improved time to market. In this research, we study the effectiveness of the 3D integration technology and the optimization opportunities which it can provide in the different layers of the memory hierarchy in cluster-based many-core platforms ranging from intra-cluster L1 to inter-cluster L2 scratchpad memories (SPMs), as well as the main memory. In addition, by moving a part of the computation to where data resides, in the 3D-stacked memory context, we demonstrate further energy and performance improvement opportunities

Out-of-Order Retirement of Instructions in Superscalar, Multithreaded, and Multicore Processors

Los procesadores superescalares actuales utilizan un reorder buffer (ROB) para contabilizar las instrucciones en vuelo. El ROB se implementa como una cola FIFO first in first out en la que las instrucciones se insertan en orden de programa después de ser decodificadas, y de la que se extraen también en orden de programa en la etapa commit. El uso de esta estructura proporciona un soporte simple para la especulación, las excepciones precisas y la reclamación de registros. Sin embargo, el hecho de retirar instrucciones en orden puede degradar las prestaciones si una operación de alta latencia está bloqueando la cabecera del ROB. Varias propuestas se han publicado atacando este problema. La mayoría utiliza retirada de instrucciones fuera de orden de forma especulativa, requiriendo almacenar puntos de recuperación (checkpoints) para restaurar un estado válido del procesador ante un fallo de especulación. Normalmente, los checkpoints necesitan implementarse con estructuras hardware costosas, y además requieren un crecimiento de otras estructuras del procesador, lo cual a su vez puede impactar en el tiempo de ciclo de reloj. Este problema afecta a muchos tipos de procesadores actuales, independientemente del número de hilos hardware (threads) y del número de núcleos de cómputo (cores) que incluyan. Esta tesis abarca el estudio de la retirada no especulativa de instrucciones fuera de orden en procesadores superescalares, multithread y multicore.Ubal Tena, R. (2010). Out-of-Order Retirement of Instructions in Superscalar, Multithreaded, and Multicore Processors [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8535Palanci

Parallel and Distributed Computing

The 14 chapters presented in this book cover a wide variety of representative works ranging from hardware design to application development. Particularly, the topics that are addressed are programmable and reconfigurable devices and systems, dependability of GPUs (General Purpose Units), network topologies, cache coherence protocols, resource allocation, scheduling algorithms, peertopeer networks, largescale network simulation, and parallel routines and algorithms. In this way, the articles included in this book constitute an excellent reference for engineers and researchers who have particular interests in each of these topics in parallel and distributed computing

Definition of a Method for the Formulation of Problems to be Solved with High Performance Computing

Computational power made available by current technology has been continuously increasing, however today’s problems are larger and more complex and demand even more computational power. Interest in computational problems has also been increasing and is an important research area in computer science. These complex problems are solved with computational models that use an underlying mathematical model and are solved using computer resources, simulation, and are run with High Performance Computing. For such computations, parallel computing has been employed to achieve high performance. This thesis identifies families of problems that can best be solved using modelling and implementation techniques of parallel computing such as message passing and shared memory. Few case studies are considered to show when the shared memory model is suitable and when the message passing model would be suitable. The models of parallel computing are implemented and evaluated using some algorithms and simulations. This thesis mainly focuses on showing the more suitable model of computing for the various scenarios in attaining High Performance Computing

Optimal digital system design in deep submicron technology

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (p. 165-174).The optimization of a digital system in deep submicron technology should be done with two basic principles: energy waste reduction and energy-delay tradeoff. Increased energy resources obtained through energy waste reduction are utilized through energy-delay tradeoffs. The previous practice of obliviously pursuing performance has led to the rapid increase in energy consumption. While energy waste due to unnecessary switching could be reduced with small increases in logic complexity, leakage energy waste still remains as a major design challenge. We find that fine-grain dynamic leakage reduction (FG-DLR), turning off small subblocks for short idle intervals, is the key for successful leakage energy saving. We introduce an FG-DLR circuit technique, Leakage Biasing, which uses leakage currents themselves to bias the circuit into the minimum leakage state, and apply it to primary SRAM arrays for bitline leakage reduction (Leakage-Biased Bitlines) and to domino logic (Leakage-Biased Domino). We also introduce another FG-DLR circuit technique, Dynamic Resizing, which dynamically downsizes transistors on idle paths while maintaining the performance along active critical paths, and apply it to static CMOS circuits.(cont.) We show that significant energy reduction can be achieved at the same computation throughput and communication bandwidth by pipelining logic gates and wires. We find that energy saved by pipelining datapaths is eventually limited by latch energy overhead, leading to a power-optimal pipelining. Structuring global wires into on-chip networks provides a better environment for pipelining and leakage energy saving. We show that the energy-efficiency increase through replacement with dynamically packet-routed networks is bounded by router energy overhead. Finally, we provide a way of relaxing the peak power constraint. We evaluate the use of Activity Migration (AM) for hot spot removal. AM spreads heat by transporting computation to a different location on the die. We show that AM can be used either to increase the power that can be dissipated by a given package, or to lower the operating temperature and hence the operating energy.by Seongmoo Heo.Ph.D

Efficient Scaling of Out-of-Order Processor Resources

Rather than improving single-threaded performance, with the dawn of the multi-core era, processor microarchitects have exploited Moore's law transistor scaling by increasing core density on a chip and increasing the number of thread contexts within a core. However, single-thread performance and efficiency is still very relevant in the power-constrained multi-core era, as increasing core counts do not yield corresponding performance improvements under real thermal and thread-level constraints. This dissertation provides a detailed study of register reference count structures and its application to both conventional and non-conventional, latency tolerant, out-of-order processors. Prior work has incorporated reference counting, but without a detailed implementation or energy model. This dissertation presents a working implementation of reference count structures and shows the overheads are low and can be recouped by the techniques enabled in high-performance out-of-order processors. A study of register allocation algorithms exploits register file occupancy to reduce power consumption by dynamically resizing the register file, which is especially important in the face of wider multi-threaded processors who require larger register files. Latency tolerance has been introduced as a technique to improve single threaded performance by removing cache-miss dependent instructions from the execution pipeline until the miss returns. This dissertation introduces a microarchitecture with a predictive approach to identify long-latency loads, and reduce the energy cost and overhead of scaling the instruction window inherent in latency tolerant microarchitectures. The key features include a front-end predictive slice-out mechanism and in-order queue structure along with mechanisms to reduce the energy cost and register-file usage of executing instructions. Cycle-level simulation shows improved performance and reduced energy delay for memory-bound workloads. Both techniques scale processor resources, addressing register file inefficiency and the allocation of processor resources to instructions during low ILP regions.Ph.D., Computer Engineering -- Drexel University, 201

Cooperative Data and Computation Partitioning for Decentralized Architectures.

Scalability of future wide-issue processor designs is severely hampered by the use of centralized resources such as register files, memories and interconnect networks. While the use of centralized resources eases both hardware design and compiler code generation efforts, they can become performance bottlenecks as access latencies increase with larger designs. The natural solution to this problem is to adapt the architecture to use smaller, decentralized resources. Decentralized architectures use smaller, faster components and exploit distributed instruction-level parallelism across the resources. A multicluster architecture is an example of such a decentralized processor, where subsets of smaller register files, functional units, and memories are grouped together in a tightly coupled unit, forming a cluster. These clusters can then be replicated and connected together to form a scalable, high-performance architecture. The main difficulty with decentralized architectures resides in compiler code generation. In a centralized Very Long Instruction Word (VLIW) processor, the compiler must statically schedule each operation to both a functional unit and a time slot for execution. In contrast, for a decentralized multicluster VLIW, the compiler must consider the additional effects of cluster assignment, recognizing that communication between clusters will result in a delay penalty. In addition, if the multicluster processor also has partitioned data memories, the compiler has the additional task of assigning data objects to their respective memories. Each decision, of cluster, functional unit, memory, and time slot, are highly interrelated and can have dramatic effects on the best choice for every other decision. This dissertation addresses the issues of extracting and exploiting inherent parallelism across decentralized resources through compiler analysis and code generation techniques. First, a static analysis technique to partition data objects is presented, which maps data objects to decentralized scratchpad memories. Second, an alternative profile-guided technique for memory partitioning is presented which can effectively map data access operations onto distributed caches. Finally, a detailed, resource-aware partitioning algorithm is presented which can effectively split computation operations of an application across a set of processing elements. These partitioners work in tandem to create a high-performance partition assignment of both memory and computation operations for decentralized multicluster or multicore processors.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/57649/2/mchu_1.pd