Gpu cache policy, Optimizing these workloads is important, but complicated
Gpu cache policy, Similar to CPUs, GPU caches utilize a Least Recently Used (LRU) replacement policy [8, 56]. To study this, we Processing elements such as CPUs and GPUs depend on cache technology to bridge the classic processor memory subsystem performance gap. It adaptively bypasses the GPU cache for blocks that are unlikely to be referenced again before being evicted. Multiply by hundreds of concurrent requests, and the KV cache, not the model weights, becomes the dominant consumer of GPU memory. Optimizing these workloads is important, but complicated. For a 70B model with long context windows, a single request’s KV cache can consume multiple gigabytes. Sep 30, 2019 · In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. As memory demands grow and data movement overheads increasingly limit performance, determining the best GPU caching policy to use for a diverse range of MI workloads represents one important challenge. On CPUs only a few Sep 30, 2019 · As memory demands grow and data movement overheads increasingly limit performance, determining the best GPU caching policy to use for a diverse range of MI workloads represents one important challenge. Sep 5, 2017 · These blocks waste cache memory space, resulting in reduced GPU performance. We propose a GPU cache management technique to im-prove the e ciency of small GPU caches while further re-ducing their power consumption. We propose old Tree-based PLRU on two-level caches with higher speed up or performance matching of LRU at GPUs. . We also investigate the throughput and access latency of GPU global memory and shared memory. To study this, we evaluate 17 MI applications and characterize their behaviors using a range of GPU caching strategies. NVIDIA GPUs, for example, employ a two-level cache hierarchy: each Streaming Multiprocessor (SM) has a private L1 cache, and all SMs share the L2 cache. We would like to show you a description here but the site won’t allow us. This technique saves energy by avoid-ing needless insertions and evictions while avoiding cache pollution, resulting in better performance. Conventional LRU replacement policy cannot consider the problems from non-reused cache blocks and frequently-reused cache blocks. The existing cache partitioning algorithms assume Least Recently Used (LRU) as underlying replacement policy. As GPUs evolve into general purpose co-processors with CPUs sharing the load, good cache design and use becomes increasingly important. When memory fills, the system must reject requests, evict cached sequences, or recompute from scratch. We show Apr 28, 2016 · Specifically, we investigate the structures of different GPU cache systems, such as the data cache, the texture cache and the translation look-aside buffer (TLB). This policy estimates which cache Of course, caches have a much smaller capacity than the memory size of the system, so the currently cached data set continuously changes according to the memory access pattern of the executed code and the replacement policy implemented by the cache. [p142] But on the other page, it says: Global memory loads/stores are staged through caches. In recent years, machine intelligence (MI) applications have emerged as a major driver for the computing industry. Oct 17, 2025 · Cache plays a critical role in GPU architecture and is essential for achieving high performance in GPU applications. In this paper, a new cache replacement policy based on the reuse pattern of cache blocks is proposed. To study this, we Feb 27, 2025 · It says about the GPU cache: On the CPU, both memory loads and stores can be cached. Optimizing these workloads is important but complicated. [p158] I'm really confused whether the GPU cache the store or not. However, on the GPU only memory load operations can be cached; memory store operations cannot be cached. While both CPUs and GPUs must cooperate and perform well, their memory access patterns are very different.
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