Standard C++ allocator benchmarks measure throughput under synthetic load but ignore how mixed object lifetimes fragment the heap over long runtimes. Maksim Martynov, Lead Programmer at Playrix (Homescapes), published two peer-reviewed papers formalizing object lifetime as the primary experimental variable, introducing phase decomposition and lifetime-policy models that let teams diagnose memory degradation before it becomes a production incident.
A lead programmer at one of the world’s top mobile game publishers developed an original methodology that treats object lifetime as a primary experimental variable in C++ allocator evaluation, closing a gap that standard frameworks have left open
Memory is where a lot of C++ performance quietly goes wrong. In one developer survey, 55% named memory-related issues as a leading source of performance bottlenecks, and the trouble usually stays hidden until production. Allocator choice gets treated as a narrow implementation detail, performance looks fine in testing, and then it degrades once real users introduce the one variable benchmarks are rarely built to measure over: time.
Maksim Martynov has spent his career on the production side of that gap. A lead programmer at Playrix, one of the world’s highest-grossing mobile game studios and the maker of Homescapes, he heads one of the gameplay engineering teams working on that product. Over seven years, there he moved from C++ developer to Lead Programmer (C++), running the programming team within a cross-functional product team and rebuilding core architectural components on a product that serves millions of players a day. He has also taken the problem into research, publishing two peer-reviewed papers that turn object lifetime, the variable that those benchmarks ignore, into something explicit and measurable. The work is an attempt to bring rigor to a corner of systems engineering that has long run on intuition and folklore.
The standard way to evaluate a memory allocator is to measure throughput under synthetic load: allocations per second, how median latency behaves, what happens under contention. Benchmarks like the widely used Larson test and Google Threadtest are useful as far as they go. What they do not capture is how an allocator behaves when objects with different lifespans share the same memory region over a long runtime. That is where Martynov’s methodology paper begins.
In “Methodology for the Experimental Evaluation of Memory Allocation Strategies and Object Lifetime Models in C++ Systems,” published in 2026 in the Universal Library of Engineering Technology, he targets a gap those frameworks leave open. The Larson test and Google Threadtest measure throughput under contention but treat object lifetime as an invisible variable, which leaves them blind to the fragmentation patterns that build up over time. His methodology makes object lifetime the primary experimental parameter, formalizing five lifetime policies (FIFO, LIFO, Random, Bounded and LongLived) and treating their interaction as the main driver of heap degradation.
The mechanism is not subtle once you see it. Long-lived objects lock memory pages the allocator cannot return to the OS. Short-lived objects keep cycling around them. The heap fragments because of the structural incompatibility of mixing objects with different temporal profiles inside the same arena, not because of any single bad allocation. Fragmentation, the paper argues, comes specifically from this intermingling, and separating arenas by lifetime profile is the intervention that stabilizes the memory footprint.
Most teams find the problem too late. A game that runs cleanly in a 10-minute test can show steadily growing memory pressure after 45 minutes of live play, and by the time the symptom appears, the allocation architecture that caused it is deep in the codebase.
Maksim came to systems engineering through an unconventional route. Twelve years in commercial management, then a deliberate pivot into software development through self-taught indie projects left a mark on how he frames failure.
“Management teaches you that systems don’t fail at launch. I witnessed them fail at month seven, when the processes that held together under pressure started to fray. Memory works the same way. A benchmark that runs for ten minutes tells you almost nothing useful about production behavior,” he says.
One of the more practically useful elements of Maksim’s methodology is its phase decomposition model. The framework separates experimental evaluation into three structural phases: RampUp (heap initialization, warming up the live set), Steady (stable load where allocations and deallocations balance), and BulkReclaim (mass object destruction, as happens during a game scene transition or request completion). Within Steady, a Churn workload profile can be applied to model intensive object rotation under constant memory volume. This pattern is most characteristic of a game engine processing frames at high frequency.
Until now, allocator evaluation had no standard way to isolate these regimes from one another. Churn in Martynov’s model is not a structural phase but a workload profile applied within Steady, which means intensive object rotation is measured against a stable baseline rather than conflated with initialization or reclamation costs. The framework can therefore attribute degradation to a specific phase and lifetime policy combination.
Different allocators fail in different phases, and averaging across them hides the failure mode. An allocator that performs well under Steady load may handle BulkReclaim poorly, retaining freed pages in internal pools rather than returning them to the OS, causing the process’s apparent memory footprint to remain elevated even when the live object set is near zero. Conversely, an allocator that looks expensive during RampUp may offer the most predictable behavior under sustained churn. Two allocators that look identical in aggregate may be failing in entirely different places: one during initialization, one during bulk reclamation. Without this structure, the comparison tells you nothing actionable about where to intervene.
His other scientific work, “Architectural Patterns of Specialized Memory Allocators in C++ and Trade-Offs between Latency and Fragmentation,” published in the same international journal with free access available for other professionals, approaches the problem from a different angle, which the existing literature, focused largely on benchmark ranking and library comparison, had left largely unaddressed. Instead of evaluating allocators in isolation, he maps allocator patterns, such as bump/arena, pool, stack, segregated-list, thread-local distributed heaps, reclamation-assisted designs, onto workload conditions through what the paper calls a “temporal architecture of reuse.” His core argument is that low allocation latency is not a stable property of an allocator. It is a conditional property that holds as long as the allocator’s embedded assumptions about object lifetime match the actual workload. Once those assumptions drift, the latency gain gets repaid through stranded memory, locality degradation, or deferred reclamation backlog, costs that accumulate invisibly until they become a production incident.
A Bug That Isn’t There
To make the mechanism concrete, Martynov’s methodology walks through a purpose-built hypothetical. A C++ trading gateway receives 50,000 market quotes per second, each living approximately two milliseconds, sharing a heap with a long-lived instrument directory and a pool of network buffers. The paper uses this scenario to illustrate how mixed-lifetime allocations cause reserved memory to grow far beyond the live set, not because of any bug, but because long-lived objects hold pages that short-lived objects can no longer reclaim. Separating object categories by lifetime profile, routing each to its own allocator, reduces retention and stabilizes the memory footprint. A structural problem with a structural fix.
“At the lead level, architecture, team management, and product delivery have deadlines that don’t coordinate. You can design the right memory model and still ship the wrong thing because you didn’t protect your team’s focus long enough to implement it. Technical debt, almost always, is a prioritization failure in disguise,” Maksim notes.
At Playrix, he has led architectural refactoring in core gameplay systems, focusing on reducing technical debt and maintaining stability as the product evolves. His allocator research applies the same broader principle to memory architecture: assumptions must be revisited as workloads change.
Maksim’s work also points to something broader. Without a structured framework for evaluating how allocators behave over time, teams have no systematic basis for diagnosing memory degradation before it becomes a production incident. They haven’t got a repeatable way to act on what they find either. That gap widens on its own. Codebases grow and workloads change, while the memory architecture that made sense at launch quietly becomes less suited to the product it’s running. It does not self-correct.
The result is a lopsided market: plenty of junior and generalist developers, and a real, growing shortage of senior engineers who can run complex systems in production. In performance-critical C++ work, that shortage is sharpened by how deep the specialization goes. Engineers who can reason about allocation strategies, object-lifetime models and their interaction under production load are a small subset of an already small group, and a hiring pipeline increasingly tuned for managed languages and AI tooling is not producing more of them.
Maksim Martynov works on this inside his own team through structured code reviews, systematic engineering feedback, and a turn as technical evaluator in hiring. The papers are an extension of the same instinct: taking tacit knowledge and making it explicit enough to teach. His own path is part of the point.
“Starting in engineering after 12 years in management meant starting at zero. But it also meant arriving with a different kind of systems thinking, built around how organizations fail over time. The two turn out to be closer than they look,” he says.
Failure modes that look like mysterious late-in-lifecycle degradation become diagnosable once allocator evaluation is treated as a reproducible process rather than tribal knowledge. The point of formalizing it is practical: teams can catch drift before it becomes an incident, revise the architecture when the workload changes, and narrow the gap between benchmark performance and production reality, one of the more expensive gaps in systems engineering. The knowledge to close it already exists. Written down and reviewed, it stops living inside individual engineers and becomes something a team can pass on.
Get the most important tech news in your inbox each week.
Contributed article. Not produced by the TNW newsroom and does not reflect the editorial stance of TNW.