Imagine the frustration of spending more time waiting for data than actually using it. From punchcards to cloud computing, the story of computing has been architectural quest to avoid such information bottlenecks.
Welcome aboard a whirlwind tour tracing innovations that transformed memory from an extravagance into abundant commodity! This ride promises surprising twists plus optimization tips allowing your apps to thrive in an era of big data.
We‘ll stretch across seven decades while exploring these milestones:
- Core breakthroughs proliferating memory density
- Principle governing efficient designs – locality of reference
- Organization for balancing speed, scale, price – hierarchy
- Programming tactics harnessing advances for max performance
- Cutting edge persistent memory changing storage rules
I will adopt a friendly teacher role focused on transferring insights rather than just information. Let‘s start our journey by visiting the era where even affording memory meant difficult choices!
The Memory Pioneers: Trailblazers Who Paid Big for Small Storage
The 1940s and early 50s were an age of memory scarcity even at huge expense. Mavericks like Maurice Wilkes, designer of the EDSAC, agonized over building vs renting precious storage given the staggering prices.
Cost for the EDSAC‘s 512 17-bit words in ultrasonic delay line memory? Nearly $700 per word in today‘s money! Despite the pain, such visionaries persevered through craft and conviction.
Landmark memory advances, triggered through the late 1950s, help quantify the pioneers‘ contributions:
| Year | Innovation | Size | Speed (cycles) | Cost/bit |
| 1955 | Ferrite core | kb-Mb | 6usec | $1 |
| 1960 | DRAM | kb | 1usec | 0.01cent |
| 1970 | NMOS Dynamic | Mb | .3usec | .001cent |
Punchcard woes faded as capacities raced upward while density doubled every two years. Visionaries had triggered an era where memory enhanced rather than obstructed computation!
Thank Goodness for Locality!
What made such prolific growth feasible? Beyond clever engineering, data access itself has an innate structure that designers leveraged called locality.
Observe our data interaction patterns and the principle of locality emerges – programs access same locations repeatedly over short intervals. We see it with variables used together within loops, functions operating on related data structures and critical operating parameters referenced frequently. This manifests as spatial and temporal locality.
Such concentrated access allows fast, small memories address hot data handling majority of operations. Capacity demands get tempered since all data need not be instantly accessible. Hierarchy organizes subsystems by successfully exploiting locality – let‘s see how!
Memory Hierarchy to the Rescue
Harnessing locality, memory hierarchy places speed optimized but capacity constrained components up top while slower devices with massive space get layered below.
This helps balance cost and performance by reducing the dependency on large expensive modules to meet every data requirement immediately. Data gets dynamically staged through the layers anticipating patterns.
Here‘s a typical hierarchy visualized:
Registers and cache form fastest access upper layers, while virtual memory and drives compose larger secondary storage. Specialized controllers orchestrate movement across this spectrum.
Now we dive into the individual tiers while inspecting their unique traits.
Registers – The First Stop for Data
Microprocessors contain extremely fast storage directly within ALUs called registers – a tiny scratchpad for urgent data. Operations source operands from registers and place results back minimizing external reads and writes.
While measured in mere bytes, a large register file size like AMD‘s Zen 4 (184 entry) and massive (>300GB/s) access speeds make them ideal latency fighters. Multiported designs allow several contemporaneous accesses, enabling advanced optimizations like register renaming and out-of-order execution.
Despite their advantages, practical constraints on silicon budgets mean registers get supplemented by smarter memories…
Cache – Staging Data Near CPU
While we play memory capacity catch up, the processor also gets increasingly faster. Without cache, it could stall waiting for larger memories.
Instead cache memories transparently handle an exponential rise in latency gaps through locality aware designs. Hot data gets staged closer through intelligent controllers, aided by predictive prefetching algorithms.
Levels partition cache optimizing for access times vs hit rates while associativity boosts effectiveness. With caches occupying 50%+ silicon real estate in modern chips, massive resources dedicated show their performance impact!
Virtual memory next injects secondary storage into the act…
Virtual Memory – Limitless Address Spaces
Imagine running out of memory in the middle of an important task. Virtual memory (VM) saves the day by extending volatile physical RAM with solid state/hard drive paging backing.
via demand paging, inactive data gets evicted from memory-mapped address spaces only to be reloaded later after a page fault. Sophisticated kernel policies attempt minimizing such expensive disk access through page replacement algorithms like LRU and preemptive migrations between memory and cache.
Segmentation further aids VM efficiency by allowing non-contiguous address spaces. 64-bit architectures support practically unlimited theoretical pools on top of 4-8GB physical RAM.
This lifesaver however has a catch due to slower hard drives…
Storage – Cheap and Deep
ROTATING drives with mechanical seeking form storage beds rock bottom the hierarchy – maximizing density and capacity over speed. Bus widths up to 24Gb/s now match internal transfer rates enabling huge pooled volumes.
SSDs boost access performance while retaining HDD density benefits – replacing mechanically seeking heads with electronic NAND lookup. NVMe interconnect eliminates protocol overhead through direct PCIe x4/x8 attachment on modern systems, unlocking GB/sec ssequential throughput matched by emerging storage classes like Intel‘s Optane.
This brings us to our next evolutionary chapter promising richer memories…
Persistent Memory – Fundamentally Reshaping Hierarchy?
Non-volatile memories like 3D XPoint medium term storage retaining data sans power with near DRAM performance. Such persistent memory offers an intriguing alternative straddling worlds of storage and memory.
The Future – Processing Near Data
Optane DIMMS allow memory mapping PM right onto the memory bus promising bonkers fast access. Computational Storage drives embed processing within SSDs aiming workload synergies through near data processing. Memory semantics persist across power cycles reducing latency by avoiding serialization delays.
This combination of high density and speed threatens to shake up memory orthodoxy! While pricing remains key arbiter for mass adoption, platform support from Intel and AMD makes this space worth tracking.
Now that we have sufficient background on the terrain, let‘s shift gears into optimization techniques!
Locality Optimized Code – Friendlier Programs Through Conscious Caching
Our journey has highlighted the pivotal need for localized accesses minimizing external references. This section collects programming tactics improving memory cooperation by reducing high-latency events, avoiding forced evictions while engineering cache hits.
Such optimizations manifest via:
- Sequential access over random walks
- Grouping commonly used data structures
- Padding to prevent displacement conflicts
- Loop blocking for cache-sized chunks
- NUMA first strategies on large servers
For illustration, here‘s contrasting code snippets highlighting better vs worse examples:
Bad:
for(j=0;j<1000;j++){
Access A[j]
Access B[j]
}
for(j=0;j<1000;j++) {
Access B[j]
Access C[j]
}The disjoint loops cause expensive evictions and misses.
Good:
for(j=0; j<1000 ; j++){
Access A[j]
Access B[j]
Access C[j]
}Grouping together boosts locality. Profilers like Cachegrind check improvements…
There are rich tactical reserves directly applicable so do master them!
The Future Beckons – Come Onboard!
Our breakneck sprint across memory technologies should hopefully reveal the sustained innovation driving this domain. While speeds and feeds will keep changing, fundamental forces like the locality principle and organizing logic behind hierarchical designs remain remarkably constant.
Rather than getting distracted by Nicer caches, tighter timings or denser Optane DIMMS alone, programmers are best served through holistic appraisal of workload access patterns and data lifetimes relative to the system memory map. Architectures co-evolve with software – by co-designing for locality around how modern machines access data, vast performance riches remain within reach!
So get hacking, and here‘s raising a toast to many glorious memory revolutions ahead 🙂
