Learning Assisted Decoupled Software Pipelining (LA-DSWP)

In this thesis, I introduce and implement an extension to the Decoupled Software Pipelining (DSWP) algorithm proposed by Rangan et al. This new extension is named Learning Assisted Decoupled Software Pipelining (or LA-DSWP) as it applies reinforcement learning to the partitioning problem found within DSWP. Through experimentation, the viability of DSWP and LA-DSWP as optimizations that produce significant program speedup is tested and measured. As computer architects strive to keep up with public expectations for processor performance growth, they are increasingly turning to processor designs which utilize multiple independent cores on a single chip. Unlike most prior hardware innovations, computer programs must be written or compiled with multiple threads in mind to take advantage of these new hardware innovations. Automatic thread-extraction using Decoupled Software Pipelining seeks to extract multiple threads from a single-threaded program~\cite{ottoni-micro-2005}. This is done by allowing loops within the program to execute on multiple cores on a single processor chip simultaneously without programmer intervention. DSWP focuses on splitting large recursive data structure's traversal loops into multiple threads in an attempt to increase overall program performance. Unlike prior implementations of DSWP, this research uses a hardware and language independent implementation of DSWP using the LLVM framework. Rather than relying on custom-built hardware to facilitate communication between program threads, this implementation uses Intel's Thread Building Blocks library to create queues in the shared memory between the various on-chip processor cores. As this thesis will show, this design setup relies heavily on the memory subsystem of the targeted processors and is greatly impacted by the actual design of the memory subsystem. Another novel addition to DSWP explored in this thesis is the application of machine learning to the partitioning process. Instead of partitioning the nodes of a loop's program dependency graph using predefined heuristics, this thesis seeks to apply reinforcement learning to allow the DSWP agent to make more informed decisions when optimizing a given loop. The DSWP agent is able to collect and analyze data about each node of a program's loop to partition the loop on a node-by-node basis. This addition constitutes LA-DSWP. Through experimentation on modern Intel processors, this thesis tests the feasibility of LA-DSWP on current hardware. Multiple kernel programs were written to search for program patterns that can achieve performance increases using DSWP partitioning. Experiments were run using the partitioning methods discussed in earlier papers along with the proposed method utilizing machine learning

Reusing cached schedules in an out-of-order processor with in-order issue logic

Modern processors use out-of-order processing logic to achieve high performance in Instructions Per Cycle (IPC) but this logic has a serious impact on the achievable frequency. In order to get better performance out of smaller transistors there is a trend to increase the number of cores per die instead of making the cores themselves bigger. Moreover, for throughput-oriented and server workloads, simpler in-order processors that allow more cores per die and higher design frequencies are becoming the preferred choice. Unfortunately, for other workloads this type of cores result in a lower single thread performance. There are many workloads where it is still important to achieve good single thread performance. In this thesis we present the ReLaSch processor. Its aim is to enable high IPC cores capable of running at high clock frequencies by processing the instructions using simple superscalar in-order issue logic and caching instruction groups that are dynamically scheduled in hardware after commit, that is, out of the critical path and only when really needed. Objective This thesis has several research goals: • Show that the dynamic scheduler of a conventional out-of-order processor does a lot of redundant work because it ignores the repetitiveness of code. • Propose a complete superscalar out-of-order architecture that reduces the amount of redundant work done by creating the schedules once in dedicated hardware, storing them in a cache of schedules and reusing the schedules as much as possible. • Place the scheduler out of the critical path of execution, which should be enabled by the reduction of work that it must do. Thus, the execution path of our proposed processor can be simpler than that of a conventional out-of-order processor. Proposal and results We present the \textbf{ReLaSch} processor, named after Reused Late Schedules, in which the creation of issue-groups is removed from the critical path of execution and uses a simple and small in-order issue logic. It just wakes-up and selects the instructions of a single issue-group each cycle, instead of processing the instructions of a whole issue queue. A new logic at the end of the conventional pipeline schedules the committed instructions. The new scheduler can be complex since it is not in the critical path of execution. The schedules are cached and whenever it is possible an rgroup is read and its instructions executed. The schedules are reused, lowering the pressure on the scheduling logic. In some cases, the ReLaSch processor is able to outperform a conventional out-of-order processor, because the post-commit scheduler has a broader vision of the code. For instance, while ReLaSch can schedule together two independent instructions that are distant in the code, a conventional out-oforder processor only issues them in the same cycle if both are in-flight. The ReLaSch processor predicts the branch targets, memory aliases and latencies at scheduling time, out of the critical path. The prediction is based on the most recent executions at scheduling time. Furthermore, most of the register renaming process is performed by the scheduler and is removed from the execution pipeline. Our experiments show that ReLaSch has the same average IPC as our reference out-of-order processor and is clearly better than the reference inorder processor (1.55 speed-up). In all cases it outperforms the in-order processor and in 23 benchmarks out of 40 it has a higher IPC than the reference out-of-order processor

Characterization and Avoidance of Critical Pipeline Structures in Aggressive Superscalar Processors

In recent years, with only small fractions of modern processors now accessible in a single cycle, computer architects constantly fight against propagation issues across the die. Unfortunately this trend continues to shift inward, and now the even most internal features of the pipeline are designed around communication, not computation. To address the inward creep of this constraint, this work focuses on the characterization of communication within the pipeline itself, architectural techniques to avoid it when possible, and layout co-design for early detection of problems. I present work in creating a novel detection tool for common case operand movement which can rapidly characterize an applications dataflow patterns. The results produced are suitable for exploitation as a small number of patterns can describe a significant portion of modern applications. Work on dynamic dependence collapsing takes the observations from the pattern results and shows how certain groups of operations can be dynamically grouped, avoiding unnecessary communication between individual instructions. This technique also amplifies the efficiency of pipeline data structures such as the reorder buffer, increasing both IPC and frequency. I also identify the same sets of collapsible instructions at compile time, producing the same benefits with minimal hardware complexity. This technique is also done in a backward compatible manner as the groups are exposed by simple reordering of the binarys instructions. I present aggressive pipelining approaches for these resources which avoids the critical timing often presumed necessary in aggressive superscalar processors. As these structures are designed for the worst case, pipelining them can produce greater frequency benefit than IPC loss. I also use the observation that the dynamic issue order for instructions in aggressive superscalar processors is predictable. Thus, a hardware mechanism is introduced for caching the wakeup order for groups of instructions efficiently. These wakeup vectors are then used to speculatively schedule instructions, avoiding the dynamic scheduling when it is not necessary. Finally, I present a novel approach to fast and high-quality chip layout. By allowing architects to quickly evaluate what if scenarios during early high-level design, chip designs are less likely to encounter implementation problems later in the process.Ph.D.Committee Chair: Scott Wills; Committee Member: David Schimmel; Committee Member: Gabriel Loh; Committee Member: Hsien-Hsin Lee; Committee Member: Yorai Ward

Beating in-Order Stalls With "Flea-Flicker" Two-Pass Pipelining

Accommodating the uncertain latency of load instructions is one of the most vexing problems in in-order microarchitecture design and compiler development. Compilers can generate schedules with a high degree of instruction-level parallelism but cannot effectively accommodate unanticipated latencies; incorporating traditional out-of-order execution into the microarchitecture hides some of this latency but redundantly performs work done by the compiler and adds additional pipeline stages. Although effective techniques, such as prefetching and threading, have been proposed to deal with anticipable, long-latency misses, the shorter, more diffuse stalls due to difficult-to-anticipate, first- or second-level misses are less easily hidden on inorder architectures. This paper addresses this problem by proposing a microarchitectural technique, referred to as two-pass pipelining, wherein the program executes on two in-order back-end pipelines coupled by a queue. The "advance" pipeline executes instructions greedily, without stalling on unanticipated latency dependences (executing independent instructions while otherwise blocking instructions are deferred). The "backup" pipeline allows concurrent resolution of instructions that were deferred in the other pipeline, resulting in the absorption of shorter misses and the overlap of longer ones. This paper argues that this design is both achievable and a good use of transistor resources and shows results indicating that it can deliver significant speedups for in-order processor designs