Low Utilization of Resources ● Less Time (CPU Cycles) ● Less Memory (RAM) ● Less Storage (Disk) ● Less Power (Battery) ● Less Contention (Locks) ● Don’t do something more than once
Layer Inefficiencies Compound ● Inner loops are often 10–20 layers deep in call stack ● If each layer has just 50% inefficiency, ten layers will experience 60x slowdown: 1.510 = 57.6 ○ 100% inefficiency for 10 layers ⇒ 1000x slowdown: 210 = 1024 ● If a lower level is inefficient, it affects all higher levels ● A higher level may be calling an efficient lower level far too often
Algorithmic Complexity: Big O ● Many algorithms operate on N items. ● Runtime (or space) is a polynomial function of N: c k Nk + c k-1 Nk-1 + … c 1 N + c 0 ● We take the highest term, drop the constant c k , and say that the algorithm is O(Nk). ● As N grows large, runtime draws asymptotically close to Nk ● For smaller N, the constant factors matter.
Caching ● Fundamental performance technique ● Trade Space for Time ● Store results to prevent recomputation/refetching ● Caches used at all levels throughout system ● Problems: ○ Low cache hit rate; i.e., many cache misses ○ Cache replacement policy; e.g., Least Recently Used ○ Cache invalidation ○ Using too much memory
Case Study: Fibonacci ● 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, … ● F n = F n-1 + F n-2 , F 2 = F 1 = 1 ● F n ≈ φ n / √5 ○ exponential ○ φ = 1.618…, the Golden Ratio ● Naive Recursive solution is prohibitively expensive ○ Computing F n takes F n additions. ● Iterative solution is linear ● Cache is constant time (after initial calculation)
Event Latency Scaled 1 CPU Cycle 0.3 ns 1 sec Level 1 cache access 0.9 ns 3 secs Level 2 cache access 2.8 ns 9 secs Level 3 cache access 12.9 ns 43 secs Main memory access (DRAM, from CPU) 120 ns 6 mins Solid-State Disk I/O 50–150 µs 2–6 days Rotational Disk I/O 1–10ms 1–12 months Event Latency Scaled Internet: San Francisco to New York 40 ms 4 years Internet: SF to United Kingdom 81 ms 8 years Internet: SF to Australia 183 ms 19 years TCP Packet Retransmit 1–3 s 105–317 yrs OS virtualization system reboot 4 s 423 years SCSI Command Timeout 30 s 3 millennia Hardware virt. system reboot 40s 4 millennia Physical system reboot 5 min 32 millennia System Latency Source: Brendan Gregg, System Performance
Server Performance ● Throughput = # requests / request-time ● Latency = end-to-end processing time ● An assembly line is manufacturing cars. ○ It takes 8 hours to manufacture a car ○ The factory produces 120 cars per day ○ Latency: 8 hours ○ Throughput: 120 cars / day = 5 cars / hour
Premature Optimization “Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.” — Donald Knuth
Measuring Performance ● < 4% of code takes > 50% runtime ● Changes with high expectations often disappoint ● Correctness is more important than efficiency ● Metrics & Instrumentation ● Logs ● Profiling
Scalability ● Bigger Workloads ● Not synonymous with Performance ● Scale Up (Vertical) ○ More CPUs, more RAM. ○ You can’t buy (or afford) a 1,000,000-core system with petabyte RAM ● Scale Out (Horizontal) ○ Add more and more commodity machines ● Bottlenecks ● Scale Down ○ Embedded systems ○ More containers per host
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