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:  Limitations of existing cache management techniques 
1. Abstract of Cited Paper (BIB001):  The shift from GPUs to GPGPUs has brought with it many changes to the GPU architecture (e.g. more caches, more concurrent kernels, better synchronization). As GPUs press further into the general-purpose domain, architects must continue to address the performance of challenging workloads. This paper presents a set of challenge benchmarks and their key performance limitations to help direct future GPU architecture research. Our study shows GPUs must develop multiple innovative architectural techniques to efficiently execute these applications to continue making inroads into general purpose computing. 
2. Abstract of Cited Paper (BIB002):  Combining CPUs and GPUs on the same chip has become a popular architectural trend. However, these heterogeneous architectures put more pressure on shared resource management. In particular, managing the last-level cache (LLC) is very critical to performance. Lately, many researchers have proposed several shared cache management mechanisms, including dynamic cache partitioning and promotion-based cache management, but no cache management work has been done on CPU-GPU heterogeneous architectures. Sharing the LLC between CPUs and GPUs brings new challenges due to the different characteristics of CPU and GPGPU applications. Unlike most memory-intensive CPU benchmarks that hide memory latency with caching, many GPGPU applications hide memory latency by combining thread-level parallelism (TLP) and caching. In this paper, we propose a TLP-aware cache management policy for CPU-GPU heterogeneous architectures. We introduce a core-sampling mechanism to detect how caching affects the performance of a GPGPU application. Inspired by previous cache management schemes, Utility-based Cache Partitioning (UCP) and Re-Reference Interval Prediction (RRIP), we propose two new mechanisms: TAP-UCP and TAP-RRIP. TAP-UCP improves performance by 5% over UCP and 11% over LRU on 152 heterogeneous workloads, and TAP-RRIP improves performance by 9% over RRIP and 12% over LRU. 
3. Abstract of Cited Paper (BIB003):  The performance of General Purpose Graphics Processing Units (GPGPUs) is frequently limited by the off-chip memory bandwidth. To mitigate this bandwidth wall problem, recent GPUs are equipped with on-chip L1 and L2 caches. However, there has been little work for better utilizing on-chip shared caches in GPGPUs. In this paper, we propose two cache management schemes: write-buffering and read-bypassing. The write buffering technique tries to utilize the shared cache for inter-block communication, and thereby reduces the DRAM accesses as much as the capacity of the cache. The read-bypassing scheme prevents the shared cache from being polluted by streamed data that are consumed only within a thread-block. The proposed schemes can be selectively applied to global memory instructions using newly defined cache operators. We evaluate the effects of the proposed schemes for a few GPGPU applications by simulations. We have shown that the off-chip memory accesses can be successfully reduced by the proposed techniques. We also analyze the effectiveness of these methods when the throughput gap between cores and off-chip memory becomes wider. 
4. Abstract of Cited Paper (BIB004):  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. 
5. Abstract of Cited Paper (BIB005):  Initially introduced as special-purpose accelerators for games and graphics code, graphics processing units (GPUs) have emerged as widely-used high-performance parallel computing platforms. GPUs traditionally provided only software-managed local memories (or scratchpads) instead of demand-fetched caches. Increasingly, however, GPUs are being used in broader application domains where memory access patterns are both harder to analyze and harder to manage in software-controlled caches. In response, GPU vendors have included sizable demand-fetched caches in recent chip designs. Nonetheless, several problems remain. First, since these hardware caches are quite new and highly-configurable, it can be difficult to know when and how to use them; they sometimes degrade performance instead of improving it. Second, since GPU programming is quite distinct from general-purpose programming, application programmers do not yet have solid intuition about which memory reference patterns are amenable to demand-fetched caches. In response, this paper characterizes application performance on GPUs with caches and provides a taxonomy for reasoning about different types of access patterns and locality. Based on this taxonomy, we present an algorithm which can be automated and applied at compile-time to identify an application's memory access patterns and to use that information to intelligently configure cache usage to improve application performance. Experiments on real GPU systems show that our algorithm reliably predicts when GPU caches will help or hurt performance. Compared to always passively turning caches on, our method can increase the average benefit of caches from 5.8% to 18.0% for applications that have significant performance sensitivity to caching. 
6. Abstract of Cited Paper (BIB006):  Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studies have shown that it is a bandwidth-limited operation on current hardware. On cache-based architectures the main factors that influence performance are spatial locality in accessing the matrix, and temporal locality in re-using the elements of the vector. 
7. Abstract of Cited Paper (BIB007):  While scalable coherence has been extensively studied in the context of general purpose chip multiprocessors (CMPs), GPU architectures present a new set of challenges. Introducing conventional directory protocols adds unnecessary coherence traffic overhead to existing GPU applications. Moreover, these protocols increase the verification complexity of the GPU memory system. Recent research, Library Cache Coherence (LCC) [34, 54], explored the use of time-based approaches in CMP coherence protocols. This paper describes a time-based coherence framework for GPUs, called Temporal Coherence (TC), that exploits globally synchronized counters in single-chip systems to develop a streamlined GPU coherence protocol. Synchronized counters enable all coherence transitions, such as invalidation of cache blocks, to happen synchronously, eliminating all coherence traffic and protocol races. We present an implementation of TC, called TC-Weak, which eliminates LCC's trade-off between stalling stores and increasing L1 miss rates to improve performance and reduce interconnect traffic. By providing coherent L1 caches, TC-Weak improves the performance of GPU applications with inter-workgroup communication by 85% over disabling the non-coherent L1 caches in the baseline GPU. We also find that write-through protocols outperform a writeback protocol on a GPU as the latter suffers from increased traffic due to unnecessary refills of write-once data. 
8. Abstract of Cited Paper (BIB008):  Modern graphics processing units (GPUs) are delivering tremendous computing horsepower by running tens of thousands of threads concurrently. The massively parallel execution model has been effective to hide the long latency of off-chip memory accesses in graphics and other general computing applications exhibiting regular memory behaviors. With the fast-growing demand for general purpose computing on GPUs (GPGPU), GPU workloads are becoming highly diversified, and thus requiring a synergistic coordination of both computing and memory resources to unleash the computing power of GPUs. Accordingly, recent graphics processors begin to integrate an on-die level-2 (L2) cache. The huge number of threads on GPUs, however, poses significant challenges to L2 cache design. The experiments on a variety of GPGPU applications reveal that the L2 cache may or may not improve the overall performance depending on the characteristics of applications. In this paper, we propose efficient techniques to improve GPGPU performance by orchestrating both L2 cache and memory in a unified framework. The basic philosophy is to exploit the temporal locality among the massive number of concurrent memory requests and minimize the impact of memory divergence behaviors among simultaneously executed groups of threads. Our major contributions are twofold. First, a priority-based cache management is proposed to maximize the chance of frequently revisited data to be kept in the cache. Second, an effective memory scheduling is introduced to reorder memory requests in the memory controller according to the divergence behavior for reducing average waiting time of warps. Simulation results reveal that our techniques enhance the overall performance by 10% on average for memory intensive benchmarks, whereas the maximum gain can be up to 30%.

Literature Review:
The need to account for unique GPU characteristics: Conventional cache management techniques (e.g. LRU replacement policy, coherence protocols etc.) have been designed to exploit the architectural characteristics of CPU and application behavior of serial applications. However, GPU architecture and programming are significantly different from their CPU counterpart. This fact introduces several new constraints and performance-objectives and hence, the effectiveness of conventional cache management techniques is greatly reduced when they are used in GPUs BIB003 BIB004 BIB008 . Also, demand-fetched caches are only recent additions in GPUs and hence, designers have very little intuition about which applications will benefit from these caches. The need to cope with small cache size: GPU caches are shared by thousands of active threads which makes the cache a scarce resource. This may lead to cache contention in the case when the required dataset size of thread groups assigned to a SM cannot fit into the local cache BIB009 BIB005 BIB001 . In absence of caches, the bandwidth utilization of GPUs becomes very high, which significantly harms the DRAM performance due to queuing effects and memory access bursts BIB002 . The need to avoid negative effect of caches on performance: For several applications, caches may actually harm the performance BIB006 BIB007 . For example, when cache line size exceeds the minimum fetch size of main memory, unnecessary data are fetched. Similarly, with write-allocate policy, data are copied to cache on a cache miss, although they are not reused for applications with high miss-rate. Moreover, given the limitations posed by chip-area, increasing the GPU cache size reduces the area available for ALUs, which also reduces the throughput. Thus, the GPU performance cannot be improved simply by increasing the cache sizes.