VCS Constraint Profiler

Tool

VCS Constraint Profiler is a VCS profiling capability used to analyze constrained-random generator runtime and memory behavior. In the cited AMD microcode stimulus-generation case study, it reported cumulative randomize cost, individual randomize-call cost, randomize partition cost, and memory data, helping identify solver bottlenecks and compare generator architectures.

First seen 5/26/2026

Last seen 6/5/2026

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VCS Constraint Profiler

Overview

The VCS Constraint Profiler is used to analyze the performance of constrained-random generators, with reported data for both runtime and memory. In the AMD microcode stimulus-generation case study, the profiler was applied to instruction generators to understand solver cost and guide architectural changes. [C1]

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RELATIONSHIPS

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Multi-Class Randomization evaluates → 90% 7e

The VCS Constraint Profiler was used to evaluate and compare the performance of multi-class randomization.

Single-Class Randomization evaluates → 90% 7e

The VCS Constraint Profiler was used to evaluate and compare the performance of single-class randomization.

Synopsys VCS part of → 95% 4e

The VCS Constraint Profiler is a feature within Synopsys VCS.

RACE Constraint Solver uses → 88% 1e

The profiler reports performance for the RACE solver as well as the BDD solver.

RACE Solver uses → 85% 1e

The VCS Constraint Profiler evaluates performance using the RACE solver as well.

Synopsys VCS Constraint Solver part of → 85% 1e

The VCS constraint profiler is a component of the VCS toolset.

BDD Constraint Solver uses → 88% 1e

The profiler provides details about BDD solver memory usage.

BDD Solver uses → 85% 1e

The VCS Constraint Profiler provides memory details particularly useful when the BDD solver is used.

op_gen.sv uses → 92% 1e

The VCS constraint profiler reported profiling data referencing op_gen.sv.

CITATIONS

8 sources

8 citations — click to expand

[1] The VCS Constraint Profiler analyzes generator runtime and memory and reports runtime in cumulative randomize, individual randomize, and partition categories. Generating AMD microcode stimuli using VCS constraint solver

[2] The profiler identified an op_gen.sv line 4308 cumulative hotspot with 7,104 calls consuming 44 seconds, and also showed an individual 3.2-second call that occurred only twice. Generating AMD microcode stimuli using VCS constraint solver

[3] VCS partitions randomize calls when unrelated random variables can be solved independently, and the profiler reports the slowest partitions. Generating AMD microcode stimuli using VCS constraint solver

[4] Memory profile data is particularly useful with the BDD solver because it elaborates the entire solution space before selecting a solution, which can require significant memory and time, and the solution space is cached for subsequent randomizations. Generating AMD microcode stimuli using VCS constraint solver

[5] The BDD solver works well for specific architectures when memory use is not excessive and the same randomize call occurs many times, as in CPU opcode generation. Generating AMD microcode stimuli using VCS constraint solver

[6] The VCS 2009.12 release provided testcase extraction to automatically extract the slowest partition from each randomize call; profile data was used to isolate opcode randomization measurements in a small testbench. Generating AMD microcode stimuli using VCS constraint solver

[7] The multiple-class generator architecture reduced randomization problem size by splitting opcode constraints into category-specific child classes with common data and constraints in a base class. Generating AMD microcode stimuli using VCS constraint solver

[8] In the case study, the multiple-class architecture improved runtime for both solvers, with a 4x speedup for RACE and a 2x speedup for BDD, reduced BDD memory requirements, and had 7x fewer constraints than the original implementation. Generating AMD microcode stimuli using VCS constraint solver