The Precomputed-Branch architecture: Efficient branches with compiler support

Many contemporary applications feature multi-megabyte instruction footprints that overwhelm the capacity of branch target buffers (BTB) and instruction caches (L1-I), causing frequent front-end stalls that inevitably hurt performance. BTB capacity is crucial for performance as a sufficiently large BTB enables the front-end to accurately resolve the upcoming execution path and steer instruction fetch appropriately. Moreover, it also enables highly effective fetch-directed instruction prefetching that can eliminate a large portion L1-I misses. For these reasons, commercial processors allocate vast amounts of storage capacity to BTBs.This work aims to reduce BTB storage requirements by optimizing the organization of BTB entries. Our key insight is that storing branch target offsets, instead of full or compressed targets, can drastically reduce BTB storage cost as the vast majority of dynamic branches have short offsets requiring just a handful of bits to encode. Based on this insight, we size the ways of a set associative BTB to hold different number of target offset bits such that each way stores offsets within a particular range. Doing so enables a dramatic reduction in storage for target addresses. Our final design, called BTB-X, uses an 8-way set associative BTB with differently sized ways that enables it to track about 2.24x more branches than a conventional BTB and 1.3x more branches than a storage-optimized state-of-the-art BTB organization, called PDede, with the same storage budget.

With increasing use of runtime polymorphism and reliance on runtime type interpretation, the presence and importance of indirect branches has seen a considerable rise in recent workloads. Evidently, accurate target prediction for indirect branches has emerged as an important problem. While direction prediction of direct branches has received considerable research attention leading to efficient prediction policies and hardware structures implemented inside modern processors, proposals for target prediction for indirect branches has been relatively few. The problem of accurate target prediction for indirect branches is significantly tough since these transfer control to an address stored in a register that is known only at runtime. Unlike conditional direct branches, indirect branches can have more than two targets to be resolved at runtime, for which prediction requires a full 32-bit / 64-bit address to be predicted, in contrast to just a taken or not-taken decision as needed for direction prediction of direct branches. Recent research shows indirect branches, being mispredicted more frequently, can start to dominate the overall branch misprediction cost. In modern processors, the only hardware structure available to facilitate target address prediction for indirect branches is a fixed-size Branch Target Buffer (BTB). BTB is often designed as a set associative cache for storing recent target addresses for branch instructions encountered during execution, with a motivation of being able to reuse the same addresses for future instances, thereby saving latency cycles. Evidently, designing efficient indexing schemes and replacement mechanisms for BTB structures is crucial, more so, for indirect branches, since these serve as the only prediction handle. In this paper, our objective is to examine a hierarchical BTB design for multicores with total size comparable to what exists today, with a motivation towards possible improvement of prediction accuracy by facilitating collaborative constructive learning between programs executing in different cores that encounter similar histories. Specifically, we wish to propose the concept of a small on-chip L1 BTB inside each core, supported by a larger off-chip L2 BTB shared among the cores. Our motivation towards a hierarchical BTB design is twofold. On one hand, in a multiprogramming environment, where the same program is executed in multiple cores, on different test cases, there is a significant potential of target information reuse for indirect branches, as is evident from our experiments on SPEC 2006 workloads. This is typically useful for machine learning based workloads where the training phase is often executed in different cores with the same neural network being trained on different training sets. On the other hand, with different programs in different cores, there is some chance of reuse as well, due to sharing of system libraries. The essence of our idea rests on the fact that programs executed in different cores have similar history patterns that can similarly influence the target addresses. Our design aims to decrease on the on-chip L1 BTB size while investing more storage for the off-chip shared L2 BTB. Suitable allocation and replacement policies support our hierarchical design. Initial experiments show expected accuracy benefits.

Electronic systems are increasingly used for safety-critical applications, where the effects of faults must be taken under control and hopefully avoided. For this purpose, test of manufactured devices is particularly important, both at the end of the production line and during the operational phase. This paper describes a method to test the logic implementing the Branch Prediction Unit in pipelined and superscalar processors when this follows the Branch Target Buffer (BTB) architecture; the proposed approach is functional, i.e., it is based on forcing the processor to execute a suitably devised test program and observing the produced results. Experimental results are provided on the DLX processor, showing that the method can achieve a high value of stuck-at fault coverage while also testing the memory in the BTB.

Using cache-like architectural components including data caches, instruction caches, or branch target buffers as a side channel, software cache-based side-channel attacks are able to derive secret keys used in cryptographic operations through legitimate software activities. Existing software solutions are typically application specific and incur substantial performance overhead. Recent hardware proposals against attacks on data caches, although effective in reducing performance overhead, may still be vulnerable to advanced attacks. Furthermore, efficient defenses against attacks on other cache structures, including instruction caches and branch target buffers, are missing. In this paper, we propose hardware-software integrated approaches to defend against software cache-based attacks comprehensively. For attacks on data caches, we propose to use preloading, informing loads, and informing loads with software random permutation to secure the partition-locked cache (PLcache), the random permutation (RPcache) and regular caches, respectively. These approaches present different tradeoffs between hardware complexity and performance overhead. To defend against attacks on instruction caches, we show that the PLcache with preloading and the RPcache provide good protection. To defend against attacks based on branch target buffers, we propose to adopt a new update policy to eliminate potential information leaking. Our experiments show that the proposed schemes not only provide strong security protection but also incur small performance overhead.

A Branch Target Buffer (BTB) is a mechanism to support speculative execution in order to overcome the performance penalty caused by branch instructions in pipelined microprocessors. Being an intrinsically fault tolerant unit, it is hard to achieve a good fault coverage resorting to plain functional testing methods. In this paper we analyze the causes for low functional testability and propose some techniques able to effectively face these issues. In particular, we describe a strategy to perform SBST on fully associative BTB units. The unit's general structure is analyzed, a suitable test program is proposed and the strategy to observe the test responses is explained. Feasibility and effectiveness of the proposed approach are shown on a MIPS-like processor.

Instruction fetch behavior has been shown to be very regular and predictable, even for diverse application areas. In this work, we propose the Lookahead Instruction Fetch Engine (LIFE), which is designed to exploit the regularity present in instruction fetch. The nucleus of LIFE is the Tagless Hit Instruction Cache (TH-IC), a small cache that assists the instruction fetch pipeline stage as it efficiently captures information about both sequential and non-sequential transitions between instructions. TH-IC provides a considerable savings in fetch energy without incurring the performance penalty normally associated with small filter instruction caches. LIFE extends TH-IC by making use of advanced control flow metadata to further improve utilization of fetch-associated structures such as the branch predictor, branch target buffer, and return address stack. These structures are selectively disabled by LIFE when it can be determined that they are unnecessary for the following instruction to be fetched. Our results show that LIFE enables further reductions in total processor energy consumption with no impact on application execution times even for the most aggressive power-saving configuration. We also explore the use of LIFE metadata on guiding decisions further down the pipeline. Next sequential line prefetch for the data cache can be enhanced by only prefetching when the triggering instruction has been previously accessed in the TH-IC. This strategy reduces the number of useless prefetches and thus contributes to improving overall processor efficiency. LIFE enables designers to boost instruction fetch efficiency by reducing energy cost without negatively affecting performance.

Modern wide-issue superscalar processors tend to adopt deeper pipelines in order to attain high clock rates. This trend increases the number of on-the-fly instructions in processors and a mispredicted branch can result in substantial amounts of wasted work. In order to mitigate these wasted works, an accurate branch prediction is required for the high performance processors. In order to improve the prediction accuracy, we propose the bimode++ branch predictor. It is an enhanced version of the bimode branch predictor. Throughout execution from the start to the end of a program, some branch instructions have the same result at all times. These branches are defined as extremely biased branches. The bimode++ branch predictor is unique in predicting the output of an extremely biased branch with a simple hardware structure. In addition, the bimode++ branch predictor improves the accuracy using the refined indexing and a fusion function. Our experimental results with benchmarks from SpecFP, SpecINT, multi-media and server area show that the bimode++ branch predictor can reduce the misprediction rate by 13.2% to the bimode and by 32.5% to the gshare.

As the pipeline length increases, the accuracy in a branch prediction gets critical to overall performance. In designing a branch predictor, in addition to accuracy, microarchitects should consider power consumption, especially in embedded processors. In this paper, we propose a low power branch predictor, which is based on the gshare predictor, by accessing the BTB (Branch Target Buffer) only when the prediction from the PHT (Prediction History Table) is taken. To enable this, the PHT is accessed one cycle earlier to prevent the additional delay. As a side effect, two predictions from the PHT are obtained at one access to the PHT, which leads to more power reduction. The proposed branch predictor reduces the power consumption, not requiring any additional storage arrays, not incurring additional delay (except just one MUX delay) and never harming accuracy. The simulation results show that the proposed predictor reduces the power consumption by 43-52%.KeywordsPower ConsumptionPower ReductionTotal Power ConsumptionAdditional DelayEmbed ProcessorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Indirect-branch prediction is becoming more important for modern processors as more programs are written in object-oriented languages. Previous hardware-based indirect-branch predictors generally require significant hardware storage or use aggressive algorithms which make the processor front-end more complex. In this paper, we propose a fast and cost-efficient indirect-branch prediction strategy, called Target Address Pointer (TAP) Prediction. TAP Prediction reuses the history-based branch direction predictor to detect occurrences of indirect branches, and then stores indirect-branch targets in the Branch Target Buffer (BTB). The key idea of TAP Prediction is to predict the Target Address Pointers, which generate virtual addresses to index the targets stored in the BTB, rather than to predict the indirect-branch targets directly. TAP Prediction also reuses the branch direction predictor to construct several small predictors. When fetching an indirect branch, these small predictors work in parallel to generate the target address pointer. Then TAP prediction accesses the BTB to fetch the predicted indirect-branch target using the generated virtual address. This mechanism could achieve time cost comparable to that of dedicated-storage-predictors, without requiring additional large amounts of storage. Our evaluation shows that for three representative direction predictors-Hybrid, Perceptrons, and O-GEHL-TAP schemes improve performance by 18.19%, 21.52%, and 20.59%, respectively, over the baseline processor with the most commonly-used BTB prediction. Compared with previous hardware-based indirect-branch predictors, the TAP-Perceptrons scheme achieves performance improvement equivalent to that provided by a 48KB TTC predictor, and it also outperforms the VPC predictor by 14.02%.

This uses Wattch and the SPEC 2000 integer and floating-point benchmarks to explore the role of branch predictor organization in power/energy/performance trade offs for processor design. Even though the direction predictor by itself represents less than 1 percent of the processor's total power dissipation, prediction accuracy is nevertheless a powerful lever on processor behavior and program execution time. A thorough study of branch predictor organizations shows that, as a general rule, to reduce overall energy consumption in the processor, it is worthwhile to spend more power in the branch predictor if this results in more accurate predictions that improve running time. This not only improves performance, but can also improve the energy-delay product by up to 20 percent. Three techniques, however, can reduce power dissipation without harming accuracy. Banking reduces the portion of the branch predictor that is active at any one time. A new on-chip structure, the prediction probe detector (PPD), uses predecode bits to entirely eliminate unnecessary predictor and branch target buffer (BTB) accesses. Despite the extra power that must be spent accessing it, the PPD reduces local predictor power and energy dissipation by about 31 percent and overall processor power and energy dissipation by 3 percent. These savings can be further improved by using profiling to annotate branches, identifying those that are highly biased and do not require static prediction. Finally, we explore the effectiveness of a previously proposed technique, pipeline gating, and find that, even with adaptive control based on recent predictor accuracy, pipeline gating yields little or no energy savings.

Recent superscalar processors issue four instructions per cycle. These processors are also powered by highly-parallel superscalar cores. The potential performance can only be exploited when fed by high instruction bandwidth. This task is the responsibility of the instruction fetch unit. Accurate branch prediction and low I-cache miss ratios are essential for the efficient operation of the fetch unit. Several studies on cache design and branch prediction address this problem. However, these techniques are not sufficient. Even in the presence of efficient cache designs and branch prediction, the fetch unit must continuously extract multiple, non-sequential instructions from the instruction cache, realign these in the proper order, and supply them to the decoder. This paper explores solutions to this problem and presents several schemes with varying degrees of performance and cost. The most-general scheme, the collapsing buffer, achieves near-perfect performance and consistently aligns instructions in excess of 90% of the time, over a wide range of issue rates. The performance boost provided by compiler optimization techniques is also investigated. Results show that compiler optimization can significantly enhance performance across all schemes. The collapsing buffer supplemented by compiler techniques remains the best-performing mechanism. The paper closes with recommendations and suggestions for future.

When CPU enters the era of instruction level parallelism, the execution cycle of instructions has become shorter and shorter. Meanwhile, the processing speed of CPU has become faster and faster. However, only from the execution process of CPU instructions, there is a key factor that limits the speed of CPU processing instructions, namely, the branch instruction processing. The branch predictor is used to predict the execution result of the current branch instruction (taken/non-taken), which can make the program counter get the target instruction to continue to execute according to the prediction result before calculating the target jump address. If the prediction is correct, it will greatly improve the execution efficiency of CPU. This paper starts from the two basic categories of static branch prediction and dynamic branch prediction. Then analyse their prediction principle, prediction accuracy and component consumption used for prediction. For static branch predictor, its hardware consumption is less than that of dynamic branch predictor, and its accuracy is also lower than that of dynamic branch predictor. Moreover, their shortcomings are pointed out in this paper. At last, based on the original branch predictor, better branch predictors developed in recent years are introduced. In the future, considering the hardware consumption, I think the development of branch predictor will continue to optimize the branch predictor combining global history and local history, from the number of stored entries, the correlation between branch instructions and so on. On the premise of executing a large number of instructions, every little improvement in the efficiency of the predictor will bring huge benefits.

We have designed a DLX-based superscalar processor simulator. This simulator provides many more functions than its predecessors developed elsewhere. We have added trap handlers and required C functions in the system so that most of the SPEC92 programs now run on the simulator. In addition, this simulator is fully configurable and re-configurable. Specifically, the following options and functions are provided by the simulator.• Central window versus distributed reservation stations.• Branch prediction mechanisms using static or dynamic schemes. The later provides branch target buffer and branch history table.• Configurable functional units.• A fully configurable KNL non-blocking cache structure incorporated int he simulator. (K: the number of ways. N: the number of cache lines in a way. L: line size). Split versus unified option and cm + βmL/D memory latency model.• Better debugging functions allowing the interruption of the simulation, reconfiguration, restart in a cycle-by-cycle fashion, or run to the end.• NT/Win95 platform ready.This DLX-based superscalar simulator is instruction-driven, which offers richer educational features than most of the trace-driven simulators. For information about this simulator, please refer to http://com.el.yuntech.edu.tw.

Modern processors usually have a deep pipeline, superscalar architecture to obtain higher performance. As pipelines are getting deeper, accurate branch prediction is critical to achieve high performance, since fetched instructions after a branch have to be flushed from inside the pipeline when the prediction is wrong. This paper studies the performance of several types of branch predictors, starting from local branch predictor and global branch predictor. Simulation results show that the global history predictor outperforms the local history predictor due to the characteristic that branches tend to be correlated. However, the global history predictor still suffers an aliasing problem that degrades performance. Four techniques are proposed to alleviate the aliasing problem. The performance is evaluated by using Simplescalar with SPEC CINT95 benchmark programs. The proposed predictors display better performance over the conventional predictors after careful configuration of each.

Recent work in history-based branch prediction uses novel hardware structures to capture branch correlation and increase branch prediction accuracy. Branch correlation occurs when the outcome of a conditional branch can be accurately predicted by observing the outcomes of previously executed branches in the dynamic instruction stream. In this article, we show how to instrument a program so that it is practical to collect run-time statistics that indicate where branch correlation occurs, and we then show how to use these statistics to transform the program so that its static branch prediction accuracy is improved. The run-time information that we gather is called a path profile , and it summarizes how often each executed sequence of program points occurs in the program trace. Our path proles are more general than those previously proposed. The code transformation that we present is called static correlated branch prediction (SCBP). It exhibits better branch prediction accuracy than previously thought possible for static prediction techniques. Furthermore, through the use of an overpruning heuristic, we show that it is possible to determine automatically an appropriate trade-off between code expansion and branch predictability so that our transformation improves the performance of multiple-issue, deeply pipelined microprocessors like those being built today.