You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Techniques for Managing and Leveraging Caches in GPUs 
Chapter Title:  Thread Scheduling Policies 
1. Abstract of Cited Paper (BIB001):  SIMD organizations amortize the area and power of fetch, decode, and issue logic across multiple processing units in order to maximize throughput for a given area and power budget. However, throughput is reduced when a set of threads operating in lockstep (a warp) are stalled due to long latency memory accesses. The resulting idle cycles are extremely costly. Multi-threading can hide latencies by interleaving the execution of multiple warps, but deep multi-threading using many warps dramatically increases the cost of the register files (multi-threading depth SIMD width), and cache contention can make performance worse. Instead, intra-warp latency hiding should first be exploited. This allows threads that are ready but stalled by SIMD restrictions to use these idle cycles and reduces the need for multi-threading among warps. This paper introduces dynamic warp subdivision (DWS), which allows a single warp to occupy more than one slot in the scheduler without requiring extra register file space. Independent scheduling entities allow divergent branch paths to interleave their execution, and allow threads that hit to run ahead. The result is improved latency hiding and memory level parallelism (MLP). We evaluate the technique on a coherent cache hierarchy with private L1 caches and a shared L2 cache. With an area overhead of less than 1%, experiments with eight data-parallel benchmarks show our technique improves performance on average by 1.7X with energy savings of 30%. We further study the performance sensitivity of DWS to various D-cache sizes and associativities. The performance benefit of DWS is also demonstrated with various SIMD widths and multi-threading depths. While DWS may increase the number of scheduling entities, we propose ways to limit this side effect. The effectiveness of such technique is also analyzed quantitatively. 
2. Abstract of Cited Paper (BIB002):  This paper studies the effects of hardware thread scheduling on cache management in GPUs. We propose Cache-Conscious Wave front Scheduling (CCWS), an adaptive hardware mechanism that makes use of a novel intra-wave front locality detector to capture locality that is lost by other schedulers due to excessive contention for cache capacity. In contrast to improvements in the replacement policy that can better tolerate difficult access patterns, CCWS shapes the access pattern to avoid thrashing the shared L1. We show that CCWS can outperform any replacement scheme by evaluating against the Belady-optimal policy. Our evaluation demonstrates that cache efficiency and preservation of intra-wave front locality become more important as GPU computing expands beyond use in high performance computing. At an estimated cost of 0.17% total chip area, CCWS reduces the number of threads actively issued on a core when appropriate. This leads to an average 25% fewer L1 data cache misses which results in a harmonic mean 24% performance improvement over previously proposed scheduling policies across a diverse selection of cache-sensitive workloads. 
3. Abstract of Cited Paper (BIB003):  GPGPUs have emerged as one of the most widely used throughput processors. Deep multithreading and cache hierarchy are the two effective implementations to achieve high throughput computing in modern GPGPUs. However, these are two conflicting design options. Finding a proper design point between the two has become a significant performance factor to GPGPUs. This paper proposes a distributed thread scheduler for dynamic multithreading on GPGPUs. By demonstrating the trade-off issue between the multithreading and cache contention, the proposed scheduler dynamically adjusts the multithreading degree to achieve superior performance. With the proposed scheduler, the cache misses can be decreased by 20.6% and 37.9% on the L1 and L2 cache respectively. The overall performance can be enhanced by an average of 16.4%. 
4. Abstract of Cited Paper (BIB004):  This paper uses hardware thread scheduling to improve the performance and energy efficiency of divergent applications on GPUs. We propose Divergence-Aware Warp Scheduling (DAWS), which introduces a divergence-based cache footprint predictor to estimate how much L1 data cache capacity is needed to capture intra-warp locality in loops. Predictor estimates are created from an online characterization of memory divergence and runtime information about the level of control flow divergence in warps. Unlike prior work on Cache-Conscious Wave front Scheduling, which makes re­active scheduling decisions based on detected cache thrashing, DAWS makes proactive scheduling decisions based on cache us­age predictions. DAWS uses these predictions to schedule warps such that data reused by active scalar threads is unlikely to exceed the capacity of the L1 data cache. DAWS attempts to shift the burden of locality management from software to hardware, in­creasing the performance of simpler and more portable code on the GPU. We compare the execution time of two Sparse Matrix Vector Multiply implementations and show that DAWS is able to run a simple, divergent version within 4% of a performance op­timized version that has been rewritten to make use of the on-chip scratchpad and have less memory divergence. We show that DAWS achieves a harmonic mean 26% performance improvement over Cache-Conscious Wavefront Scheduling on a diverse selection of highly cache-sensitive applications, with minimal additional hardware. 
5. Abstract of Cited Paper (BIB005):  Emerging GPGPU architectures, along with programming models like CUDA and OpenCL, offer a cost-effective platform for many applications by providing high thread level parallelism at lower energy budgets. Unfortunately, for many general-purpose applications, available hardware resources of a GPGPU are not efficiently utilized, leading to lost opportunity in improving performance. A major cause of this is the inefficiency of current warp scheduling policies in tolerating long memory latencies. In this paper, we identify that the scheduling decisions made by such policies are agnostic to thread-block, or cooperative thread array (CTA), behavior, and as a result inefficient. We present a coordinated CTA-aware scheduling policy that utilizes four schemes to minimize the impact of long memory latencies. The first two schemes, CTA-aware two-level warp scheduling and locality aware warp scheduling, enhance per-core performance by effectively reducing cache contention and improving latency hiding capability. The third scheme, bank-level parallelism aware warp scheduling, improves overall GPGPU performance by enhancing DRAM bank-level parallelism. The fourth scheme employs opportunistic memory-side prefetching to further enhance performance by taking advantage of open DRAM rows. Evaluations on a 28-core GPGPU platform with highly memory-intensive applications indicate that our proposed mechanism can provide 33% average performance improvement compared to the commonly-employed round-robin warp scheduling policy. 
6. Abstract of Cited Paper (BIB006):  In this paper, we present techniques that coordinate the thread scheduling and prefetching decisions in a General Purpose Graphics Processing Unit (GPGPU) architecture to better tolerate long memory latencies. We demonstrate that existing warp scheduling policies in GPGPU architectures are unable to effectively incorporate data prefetching. The main reason is that they schedule consecutive warps, which are likely to access nearby cache blocks and thus prefetch accurately for one another, back-to-back in consecutive cycles. This either 1) causes prefetches to be generated by a warp too close to the time their corresponding addresses are actually demanded by another warp, or 2) requires sophisticated prefetcher designs to correctly predict the addresses required by a future "far-ahead" warp while executing the current warp. We propose a new prefetch-aware warp scheduling policy that overcomes these problems. The key idea is to separate in time the scheduling of consecutive warps such that they are not executed back-to-back. We show that this policy not only enables a simple prefetcher to be effective in tolerating memory latencies but also improves memory bank parallelism, even when prefetching is not employed. Experimental evaluations across a diverse set of applications on a 30-core simulated GPGPU platform demonstrate that the prefetch-aware warp scheduler provides 25% and 7% average performance improvement over baselines that employ prefetching in conjunction with, respectively, the commonly-employed round-robin scheduler or the recently-proposed two-level warp scheduler. Moreover, when prefetching is not employed, the prefetch-aware warp scheduler provides higher performance than both of these baseline schedulers as it better exploits memory bank parallelism.

Literature Review:
Yen et al. BIB004 propose a hardware-based thread scheduler to dynamically adjust the degree of multithreading in GPU with the awareness of cache contention. Their technique works in two phases. In the first phase, called training phase, the statistics are collected from the L1 and L2 cache. In the second phase, called tuning phase, PID (Proportional Integral Derivative) control is used to dynamically adjust the degree of multithreading based on the information obtained from the first phase. Thus, when the system is short of cache resources, the degree of multithreading is reduced and in the case of low cache contention, degree of multithreading is increased to benefit from the massive parallelism. Rogers et al. BIB003 propose a cache-conscious wavefront (warp) scheduling technique. Their technique uses a lost intra-wavefront locality c detector (LLD) which informs the scheduler if its decisions are destroying intra-wavefront locality. Based on this feedback, the scheduler assigns intra-wavefront locality scores to each wavefront and ensures that those wavefronts losing intra-wavefront locality are given more exclusive access to the L1 cache. Their technique effectively changes the rereference interval to reduce the number of interfering references between repeated accesses to the high locality data, which reduces the thrashing in L1 cache. Rogers et al. BIB005 propose a divergence-aware warp scheduling technique. Their technique uses a divergence-based cache footprint predictor to estimate how much L1 data cache capacity is needed to capture intra-warp locality in loops. These estimates are obtained from runtime information about the level of control flow divergence in warps and online characterization of memory divergence. Based on these estimates, warp scheduling is done in a manner that the data reused by active threads may not exceed the capacity of the L1 data cache. Thus, their technique minimizes interference in L1 cache. Jog et al. BIB006 present a coordinated CTA (cooperative thread array d ) aware c Intra-wavefront locality is termed as the locality that occurs when data is initially referenced and re-referenced from the same wavefront. d GPU applications are generally divided into several kernels, where each kernel spawns many threads. These threads are grouped together into thread blocks, which are known as cooperative thread arrays (CTAs). At the beginning of execution of an application, the CTA scheduler initiates scheduling policy that aims to minimize the impact of long memory latencies. They propose a CTA-aware two-level warp scheduler that groups all the available CTAs on a core into smaller groups and schedules all the groups (instead of individual CTAs or warps) in a round-robin fashion. This allows a smaller group of warps/threads to access the L1 cache in a particular interval of time, which reduces the cache contention. It also reduces the inactive periods since the time at which different warps reach long latency operations becomes different and hence memory stalls can be effectively hidden. To further improve the L1 hit rate, they propose a locality-aware scheduling scheme, which always prioritizes a group of CTAs in a core over the rest of the CTAs until they finish. This helps in taking advantage of the locality between nearby threads and warps which are associated with the same CTA. This is because the higher priority CTAs can keep their data in private caches and get opportunity to reuse it. Jog et al. BIB007 propose a prefetch-aware warp scheduling policy to effectively hide long memory latencies. Since consecutive warps are highly likely to access nearby cache blocks, prefetches are generated by a warp very close to the time their corresponding addresses are actually demanded by another warp. To address this, their technique separates in time the scheduling of consecutive warps such that they are not executed back-to-back. Thus, at the time when one warp stalls and generates its demand requests, a prefetcher can issue prefetches for the next N cache blocks, which are likely to be completed by the time the consecutive warps that use those blocks are scheduled. This improves the effectiveness of prefetching and also improves the L1 cache hit rate. Meng et al. BIB002 propose a technique which allows dynamic sub-division of warps for hiding latency of branch and memory divergence. In a conventional SIMD (Single instruction, multiple data) implementation, branch or memory divergence stalls an entire warp. In their technique, upon a branch divergence, a warp can be divided into two active warp-splits, each representing threads that fall into one of the branch paths. The execution of these warp-splits is then interleaved. Similarly, when threads from a single warp experience different memory-reference latencies caused by cache misses, memory latency divergence occurs. In such cases, a warp is divided into two warp-splits, one with the threads whose memory operations have completed, the other represents threads that are still stalled on cache miss. The former warp-split runs ahead and can potentially prefetch cache lines that may also be needed by threads that fell behind. Upon future memory divergence, warp-splits can be recursively divided. They also provision methods to prevent over-subdivision since it has a negative effect on performance. Guz et al. BIB001 present an analytical model to quantify the harmful effect of increasing the number of threads sharing the cache. They demonstrate that increasing the thread count improves performance until the total working set no longer fits scheduling of CTAs onto the available cores. All the threads within a CTA are executed on the same cores. in cache. Beyond this point, an increase in the number of threads degrades performance until enough threads are present to hide the system's memory latency.