Improving DRAM Performance by Parallelizing Refreshes with Accesses

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. However, there are two shortcomings of per-bank refresh. First, the per-bank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations show that our mechanisms improve system performance and energy efficiency compared to state-of-the-art refresh policies and the benefit increases as DRAM density increases.Comment: The original paper published in the International Symposium on High-Performance Computer Architecture (HPCA) contains an error. The arxiv version has an erratum that describes the error and the fix for i

The Efficacy of Error Mitigation Techniques for DRAM Retention Failures: A Comparative Experimental Study

As DRAM cells continue to shrink, they become more susceptible to retention failures. DRAM cells that permanently exhibit short retention times are fairly easy to identify and repair through the use of memory tests and row and column redundancy. However, the retention time of many cells may vary over time due to a property called Variable Retention Time (VRT). Since these cells intermittently transition between failing and non-failing states, they are particularly difficult to identify through memory tests alone. In addition, the high temperature packaging process may aggravate this problem as the susceptibility of cells to VRT increases after the assembly of DRAM chips. A promising alternative to manufacturetime testing is to detect and mitigate retention failures after the system has become operational. Such a system would require mechanisms to detect and mitigate retention failures in the field, but woul

Using Compression to Improve Chip Multiprocessor Performance

Chip multiprocessors (CMPs) combine multiple processors on a single die, typically with private level-one caches and a shared level-two cache. However, the increasing number of processors cores on a single chip increases the demand on two critical resources: the shared L2 cache capacity and the off-chip pin band-width. Demand on these critical resources is further exacerbated by latency-hiding techniques such as hardware prefetching. In this dissertation, we explore using compression to effectively increase cache and pin bandwidth resources and ultimately CMP performance. We identify two distinct and complementary designs where compression can help improve CMP perfor-mance: Cache Compression and Link Compression. Cache compression stores compressed lines in the cache, potentially increasing the effective cache size, reducing off-chip misses and improving perfor-mance. On the downside, decompression overhead can slow down cache hit latencies, possibly degrading performance. Link (i.e., off-chip interconnect) compression compresses communication messages before sending to or receiving from off-chip system components, thereby increasing the effective off-chip pin bandwidth, reducing contention and improving performance for bandwidth-limited configurations. While compression can have a positive impact on CMP performance, practical implementations of compressio

Redundancy and ECC mechanisms to improve energy efficiency of on-die interconnects

[abstract not available

Frequent Pattern Compression: A Significance-Based Compression Scheme for L2 Caches

With the widening gap between processor and memory speeds, memory system designers may find cache compression beneficial to increase cache capacity and reduce off-chip bandwidth. Most hardware compression algorithms fall into the dictionary-based category, which depend on building a dictionary and using its entries to encode repeated data values. Such algorithms are effective in compressing large data blocks and files. Cache lines, however, are typically short (32-256 bytes), and a per-line dictionary places a significant overhead that limits the compressibility and increases decompression latency of such algorithms. For such short lines, significance-based compression is an appealing alternative. We propose and evaluate a simple significance-based compression scheme that has a low compression and decompression overhead. This scheme, Frequent Pattern Compression (FPC) compresses individual cache lines on a word-by-word basis by storing common word patterns in a compressed format accompanied with an appropriate prefix. For a 64-byte cache line, compression can be completed in three cycles and decompression in five cycles, assuming 12 FO4 gate delays per cycle. We propose a compressed cache design in which data is stored in a compressed form in the L2 caches, but are uncompressed in the L1 caches. L2 cache lines are compressed to predetermined sizes that never exceed their original size to reduce decompression overhead. This simple scheme provides comparable compression ratios to more complex schemes that have higher cache hit latencies.