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In recent years, the drastically enhanced architecture and capacity of Field-Programmable Gate Array (FPGA) devices have led to the rapid growth of customized hardware acceleration for modern applications, such as machine learning, cryptocurrency mining, and high-frequency trading. However, this growing capability raises ever more challenges to FPGA placement engines. A modern FPGA device consists of heterogeneous logic resources that are unevenly distributed across the layout. This heterogeneity and nonuniformity bring difficulties to achieve smooth and high-quality placement convergences. Furthermore, FPGA devices contain complex clocking architectures to deliver flexible clock networks. The physical structure of these clock networks, however, are pre-manufactured, unadjustable, and of only limited routing resources. Conventional placement approaches without clock feasibility consideration, hence, can easily lead to clock routing failures and fail the entire FPGA implementation flow. Lastly, given the special standing of FPGAs in fast prototyping and frequent reprogramming, its implementation time is becoming a crucial determining factor to get customers’ favor. Therefore, as a runtime bottleneck of the FPGA implementation flow, ultra-fast and efficient placement engines are also in great demand. This dissertation provides a set of placement algorithms and methodologies for large-scale heterogeneous FPGAs. To essentially improve the quality of FPGA implementation, we propose three core analytical placement engines with distinct methodologies: (1) UTPlaceF, a quadratic placer with physical-aware packing; (2) UTPlaceF-DL, a quadratic placer with simultaneous packing and legalization; (3) elfPlace, an electrostatic-based nonlinear placer. To honor the clock feasibility, we propose an efficient clock-aware placement algorithm, UTPlaceF 2.0, as well as its generalized version, UTPlaceF 2.X, which produces feasible clock routing solutions together with high-quality placement. To reduce the turn-around time of FPGA implementation, we propose an ultra-fast placement engine, UTPlaceF 3.0, which exploits the parallelism on multi-core systems. The effectiveness and efficiency of proposed approaches are demonstrated with extensive experiments on industrial-strength benchmarks.