Complexity Effective Bypass Networks

Large register file with multiple ports is a critical component of a high-performance processor. A large number of registers are necessary for processing a larger number of in-flight instructions to exploit higher instruction level parallelism (ILP). Multiple ports for a register file are necessary to support execution of multiple instructions each cycle. These necessities lead to a larger register access time. However, register access time has to be minimal to enable design of high frequency processors. Analysis of lifetime of a logical to physical register mapping reveals that there are long latencies between the times a physical register is allocated, consumed, and released. We propose a dual bank register file organization that exploits such long latencies, resulting in a large bandwidth with a reduced register access time. Implementation of one flavor of the proposed register file organization, as compared to a conventional monolithic register file, in an 8-wide out-of-order issue superscalar processor enhanced instructions per cycle (IPC) throughput up to 6% for Spec2000 applications while inducing register access time up to 22%. Another flavor of the register file organization, with a similar access time as the conventional monolithic register file, enhanced the IPC up to 15%. Thus a trade-off between register access time and ILP exploitation is shown.

Java bytecodes can be executed with the following three methods: a Java interpreter running on a particular machine interprets bytecodes; a Just-in-Time (JIT) compiler translates bytecodes to the native primitives of the particular machine and the machine executes the translated codes; and a Java processor executes bytecodes directly. The first two methods require no special hardware support for the execution of Java bytecodes and are widely used currently. The last method requires an embedded Java processor, picoJavaI or picoJavaII for instance. The picoJavaI and picoJavaII are simple pipelined processors with no ILP (instruction level parallelism) and TLP (thread level parallelism) supports. A so-called MAJC (microprocessor architecture for Java computing) design can exploit ILP and TLP by using a modified VLIW (very long instruction word) architecture and vertical multithreading technique, but it has its own instruction set and cannot execute Java bytecodes directly. In this paper, we investigate a processor architecture which can directly execute Java bytecodes meanwhile can exploit Java ILP and TLP simultaneously. The proposed processor consists of multiple slots implementing horizontal multithreading and multiple functional units shared by all threads executed in parallel. Our architectural simulation results show that the Java processor could achieve an average 20 IPC (instructions per cycle), or 7.33 EIPC (effective IPC), with 8 slots and a 4-instruction scheduling window for each slot. We also check other configurations and give the utilization of functional units as well as the performance improvement with various kinds of working loads.

Modern computational workloads demand exceptional performance and efficiency, necessitating the effective utilization of advanced CPU features such as SIMD (Single Instruction Multiple Data), instruction-level parallelism (ILP), and branch prediction. This paper explores optimization techniques that address inefficiencies at the algorithmic, architectural, and system levels, enabling software to align with hardware capabilities. Key techniques include resolving data dependencies, enhancing memory locality, utilizing compiler intrinsics,applying tail call optimizations, and employing strategies like loop unrolling, blocking, vectorization, and function inlining. Tail call optimization and breaking dependency chains are analyzed to improve parallelism and reduce processing overhead. Both manual and compiler-driven approaches are evaluated, providing insights into their trade-offs and synergies. Experimental results from benchmarks, such as matrix multiplication and particle simulations, demonstrate significant gains, with up to a 3x increase in instructions per cycle (IPC) and a 40% reduction in execution time. These findings highlight the critical role of optimizing software for architectural features like cache hierarchies, pipelining, and vector widths. This study provides techniques to maximize CPU efficiency, bridging the gap between hardware potential and software performance. Future directions include extending these methodologies to hybrid architectures like GPUs and integrating machine learning models for dynamic runtime optimization.

As Out-of-Order (OOO) cores scale to very large instruction windows to extract higher instruction level parallelism (ILP), the cost of mis-speculation increases tremendously. Branch predictors sitting at the head of the pipeline are a significant contributor to the speculatively executed code. It is critical to limit the execution time down the wrong path for the sake of performance and energy efficiency. Architects continue to increase the branch predictor sizes and improve the prediction algorithms to mitigate the increasing cost of the mis-speculations. Unfortunately, there still exists branches whose outcomes are predominantly data dependent, and that significantly contribute to the overall mispredictions. This work is the first to characterize data dependent branches at a scale spanning 100+ workloads drawn from several application categories and establish a clear motivation to address them. We find that branches which have only one load instruction feeding them and only simple operations to compute the branch direction from the load value are responsible for a significant fraction of the overall mispredictions. We call such branches Direct Data Dependent (3D) branches. We develop a family of synergistic techniques to avoid mispredictions from such 3D-branches. We describe 3D-Branch Overrider, our novel branch overriding technique that operates in the front-end of the pipeline to minimize the branch misprediction penalties. On a modern Icelake-like OOO core equipped with the state-of-the-art branch predictor, we find that our technique provides a 12.7% reduction in branch mispredictions resulting in 3.1% Instructions Per Cycle (IPC) gain while needing just 5.7KB in additional storage. As future cores become wider and deeper, we show that the gains from 3D-Branch Overrider nicely increases, even if the size of the underlying branch predictor is aggressively scaled.

The run-time performance of VLIW (very long instruction word) microprocessors depends heavily on the effectiveness of its associated optimizing compiler. Typical VLIW compiler phases include instruction scheduling, which maximizes instruction level parallelism (ILP), and register allocation, which minimizes data spills to external memory. If ILP is maximized without considering register constraints, high register pressure may result, leading to increased spill code and reduced run-time performance. In this paper, a new register pressure reduction technique for embedded VLIW processors is presented to control register pressure prior to instruction scheduling and register allocation. By modifying the relative ordering of operations, this technique restructures code to better reduce spills. Our technique has been implemented in Trimaran, an academic VLIW compiler, and evaluated using a series of VLIW benchmarks. Experimental results show that, on average, our algorithm reduces dynamic spills and improves overall cycle counts by 6% for a VLIW architecture with 8 functional units and 32 registers versus previous spill code reduction techniques.

Current microprocessors aggressively exploit instruction-level parallelism (ILP) through techniques such as multiple issue, dynamic scheduling, and non-blocking reads. Recent work has shown that memory latency remains a significant performance bottleneck for shared-memory multiprocessor systems built of such processors.This paper provides the first study of the effectiveness of software-controlled non-binding prefetching in shared memory multiprocessors built of state-of-the-art ILP-based processors. We find that software prefetching results in significant reductions in execution time (12% to 31%) for three out of five applications on an ILP system. However, compared to previous-generation system, software prefetching is significantly less effective in reducing the memory stall component of execution time on an ILP system. Consequently, even after adding software prefetching, memory stall time accounts for over 30% of the total execution time in four out of five applications on our ILP system.This paper also investigates the interaction of software prefetching with memory consistency models on ILP-based multiprocessors. In particular, we seek to determine whether software prefetching can equalize the performance of sequential consistency (SC) and release consistency (RC). We find that even with software prefetching, for three out of five applications, RC provides a significant reduction in execution time (15% to 40%) compared to SC.

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To improve the performance of the processors, the pipeline technique is used extensively to implement I.L.P (Instruction Level Parallelism). We measure the processor performance through the quantity of Instructions Level Parallelism represented through its design. We can counterstrike the parallelism with the aid of the execution of conditional branch instructions, which might additionally break the flow of the program execution. To overcome the branch problem, numerous ways have been suggested proactive to predict both the direction as well as the address of the executed instructions. In this paper, the FPGA ( Field Programmable Gate Arrays) based implementation of the 1-bit dynamic branch predictor is presented. The 1-bit dynamic branch predictor is developed especially for conditional branch instructions. The pipeline would not detach from the instruction queue when any conditional instructions occur as a 1-bit dynamic branch predictor implements it. This technique improves processor performance by implementing a deduction in the controlling hazard in the pipeline. Modelsim and Quartus tool is used for the design and implementation of the predictor.

Value Prediction was proposed to speculatively break true data dependencies, thereby allowing Out of Order (OOO) processors to achieve higher instruction level parallelism (ILP) and gain performance. State-of-the-art value predictors try to maximize the number of instructions that can be value predicted, with the belief that a higher coverage will unlock more ILP and increase performance. Unfortunately, this comes at increased complexity with implementations that require multiple different types of value predictors working in tandem, incurring substantial area and power cost.In this paper we motivate towards lower coverage, but focused, value prediction. Instead of aggressively increasing the coverage of value prediction, at the cost of higher area and power, we motivate refocusing value prediction as a mechanism to achieve an early execution of instructions that frequently create performance bottlenecks in the OOO processor. Since we do not aim for high coverage, our implementation is light-weight, needing just 1.2 KB of storage. Simulation results on 60 diverse workloads show that we deliver 3.3% performance gain over a baseline similar to the Intel Skylake processor. This performance gain increases substantially to 8.6% when we simulate a futuristic up-scaled version of Skylake. In contrast, for the same storage, state-of-the-art value predictors deliver a much lower speedup of 1.7% and 4.7% respectively. Notably, our proposal is similar to these predictors in performance, even when they are given nearly eight times the storage and have 60% more prediction coverage than our solution.

The technique for reducing the length of the data dependence path is presented. This technique, named tunneling-load, utilizes the register specifier buffer in order to hide the load latency, and thus reduces the length of the data dependence path. True data dependences can not be removed by any techniques such as register renaming, and are the unavoidable obstacle limiting the instruction level parallelism. The length of the data dependence path including the load instructions is longer than those of other instructions, because the latency of the load instruction is longer than those of other instructions. In order to reduce the dependence path length including the load instructions, we propose the tunneling-load technique. We have evaluated the effects of the tunneling-load, and found that in an in-order-issue superscalar plat-form the instruction level parallelism is increased by over 10%.

If-conversion is a simple yet powerful optimization that converts control dependences into data dependences. It allows elimination of branches and increases available instruction level parallelism and thus overall performance. If-conversion can either be applied alone or in combination with other techniques that increase the size of scheduling regions. The presence of hardware support for predicated execution allows if-conversion to be broadly applied in a given program. This makes it necessary to guide the optimization using heuristic estimates regarding its potential benefit. Similar to other transformations in an optimizing compiler, if-conversion in-herently suffers from phase ordering issues. Driven by these facts, we developed two algorithms for if-conversion targeting the TI TMS320C64x+ architecture within the LLVM framework. Each implementation targets a different level of code abstraction. While one targets the intermediate representation, the other addresses machine-level code. Both make use of an adapted set of estimation heuristics and prove to be successful in general, but each one exhibits different strengths and weaknesses. High-level if-conversion, applied before other control flow transformations, has more freedom to operate. But in contrast to its machine-level counter-part, which is more restricted, its estimations of runtime are less accurate. Our results from experimental evaluation show a mean speedup close to 14% for both algorithms on a set of programs from the MiBench and DSPstone benchmark suites. We give a comparison of the implemented optimizations and discuss gained insights on the topics of if-conversion, phase ordering issues and profitability analysis.

As processors continue to exploit more instruction level parallelism, greater demands are placed on the performance of the memory system. In this paper, we introduce a novel modification of the processor pipeline called memory renaming . Memory renaming applies register access techniques to load and store instructions to speed the processing of memory traffic. The approach works by accurately predicting memory communication early in the pipeline and then re - mapping the communication to fast physical registers. This work extends previous studies of data value and dependence speculation. When memory renaming is added to the processor pipeline, renaming can be applied to 30-50 % of all memory references, translating to an overall improvement in execution time of up to 14 % for current pipeline configurations. As store forward delay times grow larger, renaming support can lead to performance improvements of as much as 42 %. Furthermore, this improvement is seen across all memory segments—including the heap segment which has often been difficult to manage efficiently.

Sampling is a popular approach to collecting data for profiling and monitoring, because it has a small impact on performance and does not modify the observed application. When sampling stack traces, they can be merged into a calling context tree that shows where the application spends its time and where performance problems lie. However, Java VM implementations usually rely on safepoints for sampling stack traces. Safepoints can cause inaccuracies and have a considerable performance impact. We present a new approach that does not use safepoints, but instead relies on the operating system to take snapshots of the stack at arbitrary points. These snapshots are then asynchronously decoded to call traces, which are merged into a calling context tree. We show that we are able to decode over 90% of the snapshots, and that our approach has very small impact on performance even at high sampling rates.

Mixed-mode parallel processors can quickly switch from executing instructions in single instruction, multiple data stream (SIMD) mode to executing them in multiple instruction, multiple data stream (MIMD) mode. To fully exploit this capability, the circumstances under which each mode should be used must be understood. This paper explores mixed-mode parallelism by presenting a general overview of the trade-offs between SIMD and MIMD modes, and then characterizing in detail three of the trade-offs concerning instruction execution performance. These include the effect of overlapping control unit and processor execution in SIMD mode, performance of conditional statements, and the consequences of switching parallelism modes.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

“ARGOS” (ARGonne Operating System), which runs on a Xerox Sigma 5 hardware configuration, provides a dynamic multiprogrammed environment which supports the following: data acquisition and interactive control for numerous (currently 19) independently running on-line laboratory experiments; three interactive graphics terminals; FORTRAN IV-H executing at each of 23 remote time-shared terminals; a jobstream from open-shop batch processing; long-term low priority computations (100 CPU hours). The system guarantees the protection of each user's interests by the utilization of the hardware memory-protection feature, internal clocks and disallowing the execution of privileged instructions by user programs. The system is interrupt-driven, with each task running to completion, contingent on its priority. System resources are provided on a first come first served basis, except that rotating memory is queued by request position. System CALLs provide users full access to hardware capability, thus providing user-directed file formats and insuring a minimum of system overhead. However, at some sacrifice in overhead, the user can make use of FORTRAN record-blocking. Core memory, disk space and magnetic tape usage, are assigned dynamically. Parametrization of the system is such that terminal characteristics are specified (one parameter card/terminal) at boot-in time (once/week after preventive maintenance).