Gennady Pekhimenko Advisers: Todd C. Mowry & Onur Mutlu

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Gennady Pekhimenko Advisers: Todd C. Mowry & Onur Mutlu Linearly Compressed Pages: A Main Memory Compression Framework with Low Complexity and Low Latency Gennady Pekhimenko Advisers: Todd C. Mowry & Onur Mutlu

Executive Summary Main memory is a limited shared resource Observation: Significant data redundancy Idea: Compress data in main memory Problem: How to avoid latency increase? Solution: Linearly Compressed Pages (LCP): fixed-size cache line granularity compression 1. Increases capacity (69% on average) 2. Decreases bandwidth consumption (46%) 3. Improves overall performance (9.5%)

Challenges in Main Memory Compression Address Computation Mapping and Fragmentation Physically Tagged Caches

Address Computation Uncompressed Page Compressed Page Cache Line (64B) . . . LN-1 Address Offset 64 128 (N-1)*64 Compressed Page L0 L1 L2 . . . LN-1 Address Offset ? ? ?

Mapping and Fragmentation Virtual Page (4kB) Virtual Address Physical Address Physical Page (? kB) Fragmentation

Physically Tagged Caches Virtual Address Core Critical Path Address Translation TLB Physical Address tag data L2 Cache Lines tag data tag data

Shortcomings of Prior Work Compression Mechanisms Access Latency Decompression Complexity Ratio IBM MXT [IBM J.R.D. ’01]  

Shortcomings of Prior Work Compression Mechanisms Access Latency Decompression Complexity Ratio IBM MXT [IBM J.R.D. ’01]   Robust Main Memory Compression [ISCA’05]

Shortcomings of Prior Work Compression Mechanisms Access Latency Decompression Complexity Ratio IBM MXT [IBM J.R.D. ’01]   Robust Main Memory Compression [ISCA’05] LCP: Our Proposal

Linearly Compressed Pages (LCP): Key Idea Uncompressed Page (4kB: 64*64B) 64B 64B 64B 64B . . . 64B 4:1 Compression Exception Storage . . . M E Metadata (64B): ? (compressible) Compressed Data (1kB)

LCP Overview Page Table entry extension compression type and size extended physical base address Operating System management support 4 memory pools (512B, 1kB, 2kB, 4kB) Changes to cache tagging logic physical page base address + cache line index (within a page) Handling page overflows Compression algorithms: BDI [PACT’12] , FPC [ISCA’04]

LCP Optimizations Integration with cache compression Metadata cache Avoids additional requests to metadata Memory bandwidth reduction: Zero pages and zero cache lines Handled separately in TLB (1-bit) and in metadata (1-bit per cache line) Integration with cache compression BDI and FPC 1 transfer instead of 4 64B 64B 64B 64B

Methodology Simulator Workloads System Parameters x86 event-driven simulators Simics-based [Magnusson+, Computer’02] for CPU Multi2Sim [Ubal+, PACT’12] for GPU Workloads SPEC2006 benchmarks, TPC, Apache web server, GPGPU applications System Parameters L1/L2/L3 cache latencies from CACTI [Thoziyoor+, ISCA’08] 512kB - 16MB L2, simple memory model

Compression Ratio Comparison SPEC2006, databases, web workloads, 2MB L2 cache LCP-based frameworks achieve competitive average compression ratios with prior work

Bandwidth Consumption Decrease SPEC2006, databases, web workloads, 2MB L2 cache Better BPKI – bandwidth per kilo instruction – lower means better. LCP frameworks significantly reduce bandwidth (46%)

Performance Improvement Cores LCP-BDI (BDI, LCP-BDI) (BDI, LCP-BDI+FPC-fixed) 1 6.1% 9.5% 9.3% 2 13.9% 23.7% 23.6% 4 10.7% 22.6% 22.5% LCP frameworks significantly improve performance

Conclusion A new main memory compression framework called LCP(Linearly Compressed Pages) Key idea: ﬁxed size for compressed cache lines within a page and fixed compression algorithm per page LCP evaluation: Increases capacity (69% on average) Decreases bandwidth consumption (46%) Improves overall performance (9.5%) Decreases energy of the off-chip bus (37%)

Gennady Pekhimenko Advisers: Todd C. Mowry & Onur Mutlu Linearly Compressed Pages: A Main Memory Compression Framework with Low Complexity and Low Latency Gennady Pekhimenko Advisers: Todd C. Mowry & Onur Mutlu