High-performance computing using fpgas pdf

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FPGAs have historically been restricted to a narrow set of HPC applications because of . accelerated using in-socket FPGA accelerators. In-socket .. http:// 2. HIGH PERFORMANCE SCIENTIFIC COMPUTING USING FPGAS WITH IEEE. FLOATING POINT possible to implement high-performance matrix and vector kernel operations on .. available at High-Performance Reconfigurable Computers are parallel computing systems that contain multiple settings, the design uses FPGAs as coprocessors that.

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Will X. Y. Li, Rosa H. M. Chan, Wei Zhang, Chiwai Yu, Dong Song, Theodore W. Berger et al. Pages PDF · High-Performance FPGA-Accelerated. High-Performance Computing using FPGA covers the area of high performance Included format: EPUB, PDF; ebooks can be used on all reading devices. Request PDF on ResearchGate | High-performance computing using FPGAs | High-Performance Computing using FPGA covers the area of high performance.

In traditional hardware design systems, components comprise black boxes with limited internal parameterization. Boston University. The microarray data analysis kernel as originally formulated requires division. The challenge is identifying the design techniques that can extract high performance potential from the FPGA fabric. Fernandez, J.

Intel's experience with developers of airborne systems yields the following recommendations, as shown in the figure below. Recent FAA guidance has highlighted the necessity to supply additional design documentation when using a generic-purpose processor or a graphical chipset. The soft IP solution is a design path that offers a high degree of available design documentation.

Recently, Intel announced the DO certifiable soft processor: This safety-critical version is provided through our partner Hcell Engineering. To comply with level A, several teams were involved in the design, verification, and validation process.

A "V Cycle" design verification was performed. The requirement capture is done independently from design, validation, and verification. See Figure 2. The following IP cores are either being assessed for certification or are currently going through a documentation and certification process for Intel customers.

High-Performance Computing Using FPGAs

Each of these IP cores, and several others, represent customer opportunities to undergo certification with support of Intel and IP partners see Tables below.

Radar backend processing is a compute-intensive operation using various algorithms such as a FIR filter, which utilize custom pipeline parallelism. Increased performance is achieved by off loading from the host processor onto an FPGA. Military communication and weapons systems are now overwhelmingly composed of high density, modern electronic components. Developing an edge, or competitive advantage, in the military marketplace necessarily requires taking advantage of the latest technologies, fastest processing, and the highest integration of analog and digital processes to reduce detection and response times in intelligence systems and military equipment.

These same systems, however, are often brought into service and maintained over time periods that are many multiples, or even orders of magnitude, longer than the constituent components of these systems.

Achieving High Performance with FPGA-Based Computing

This inevitably creates the problem of component obsolescence, which is a primary issue in the field of logistics, and fuels entire industries of component and product emulation, reverse engineering, and code transfer and qualification. Obsolescence costs, especially for unscheduled product discontinuation or vendor dissolution due to bankruptcy or acquisition, cannot be avoided completely.

However dual-sourcing and a few other strategies including vendor support agreements, 'Last Time Buys,' and inventory banking through distributors, have been used with various degrees of supportability and cost success. An additional strategy is to perform Past Performance Assessments of vendors based on product support, and selecting the lowest risk vendor. Arguably, this strategy is not used widely enough in defense acquisitions.

A full data set to use for developing your own metrics is available on the Military Portal.

High-Performance Computing Using FPGAs | Wim Vanderbauwhede | Springer

An example of one of these metrics is shown in the diagram below. Contact us. View all applications. Learn how these powerful devices can be customized to accelerate key workloads and enable design engineers to adapt to emerging standards or changing requirements.

View all devices. Safari Chrome IE Firefox. Radar and Sensors. Electronic Warfare. Shorter Time to Market and Less Engineering Risk Intel has a complete set of intellectual property IP cores, reference designs, development kits, and system-level design tools. Secure Communication. Secure Communications Systems Secure communications design challenges apply to wired and wireless systems.

Network Encryptor. Software Defined Radio. Cryptographic Capabilities Strong encryption is key to ensuring communications and data security at ever increasing data throughput rates.

Fpgas high-performance pdf using computing

Military Temperature. To meet today's market demands, Intel offers the following devices qualified to operate at military temperatures: Intel's military COTS board partners include: BittWare BittWare designs and deploys high-end signal processing board-level solutions that significantly reduce technology risk and time-to revenue for its OEM customers.

Colorado Engineering Inc. Will X. Li, Rosa H. Berger et al. Wim Vanderbauwhede, Sai. Chalamalasetti, Martin Margala. Architecture, Arithmetic and Power Issues. A Case Study. The Convey Hybrid-Core Architecture. The Weird Case of Janus. Baity-Jesi, R. Cruz, L.

Fernandez, J. Gil-Narvion, A. Gordillo-Guerrero et al. Accelerate Communication, not Computation! Fast Fourier transforms are used to compute the 3D correlations. The preferred FPGA algorithm is based on direct summation, which, despite having higher asymptotic complexity, offers several advantages.

First, small data type sizes, such as 1-bit values for representing interior versus exterior information, offer little advantage on a microprocessor. On an FPGA, however, smaller processing elements allow for more PEs in a given amount of computing fabric, and implementing products of 1-bit values is trivial.

In addition, systolic arrays for correlation are efficient. The form we chose requires one input value and generates one output value per cycle, while holding hundreds of partial sums in on-chip registers. Finally, our implementation, after a brief setup phase, delivers one multiply-accumulate operation per clock cycle per PE, times hundreds to thousands of PEs in the computing array. While FPGA configurations resemble high-level language programs, they specify hardware, not software.

Because good computing modes for software are not necessarily good computing modes for hardware, restructuring an application can often substantially improve its performance. For example, while random-access and pointer-based data structures are staples of serial computing, they may yield poor performance on FPGAs. Streaming, systolic, and associative computing structures, and arrays of fine-grained automata, are preferable.

Finding information about a newly discovered gene or protein by searching biomedical databases for similar sequences is a fundamental bioinformatics task. The most commonly used applications are based on the basic local alignment search tool, which operates in multiple phases. BLAST first determines seeds, or good matches of short subsequences, then extends these seeds to find promising candidates, and finally processes the candidates in detail, often using dynamic programming DP methods.

The preferred method avoids random accesses into a large database; rather, it streams the database through a two-dimensional systolic array. The first dimension generates, on every cycle, the character-character match scores for a particular alignment of the sequence of interest versus the database. The second dimension processes the score sequence to find the maximal local alignment.

The tree structure keeps the hardware cost low; pipelining assures generation of maximal local alignments at the streaming rate. Certain data structures such as stacks, trees, and priority queues are ubiquitous in application programs, as are basic operations such as search, reduction, and parallel prefix, and using suffix trees. Equally ubiquitous in digital logic, the analogous structures and operations usually differ from what is obtained by directly translating software structures into hardware.

Another important bioinformatics task is analyzing DNA or protein sequences for patterns indicative of disease or other functions fundamental to cell processes. These patterns are often repetitive structures, such as tandem arrays and palindromes under various mismatch models. This is sometimes difficult to achieve with existing HPC code—for example, profiling often points to kernels that comprise just 60 to 80 percent of execution time.

The problem is especially severe with legacy codes and may require a substantial rewrite. Not all is lost, however. The nonkernel code may lend itself to substantial improvement; as its relative execution time increases, expending effort on its optimization may become worthwhile. Also, combining computations not equally amenable to FPGA acceleration may have optimized the original code; separating them can increase the acceleratable kernel. Central to computational biochemistry, molecular dynamics applications predict molecular structure and interactions.

The MD computation itself is an iterative application of Newtonian mechanics on particle ensembles and alternates between two phases: The force computation comprises several terms, some of which involve bonds. The motion update and bonded force computations are O N in the number of particles being simulated, while the nonbonded are O N log N or N 2.

The latter comprises the acceleratable kernel. Because MD codes tend to be highly complex, it is sometimes necessary to start from scratch to achieve high performance. Latency hiding is a basic technique for achieving high performance in parallel applications. Overlap between computation and communication is especially desirable. In FPGA implementations, further opportunities arise: Rather than allocating tasks to processors that must communicate with one another, latency hiding simply lays out functions on the same chip to operate in parallel.

Returning to the example of modeling molecular interactions, the docking algorithm must repeat the correlations at three-axis rotations—more than 10 4 for typical degree sampling intervals.

Implementations on sequential processors typically rotate the molecule in a step separate from the correlation. The preferred technique is based on runtime index calculation and has two distinctive features.

First, index computation can be pipelined to generate indices at operating frequency due to the predictable order of access to voxels. Computations often consist of independent function sequences, such as a signal passing through a series of filters and transformations.

Multiprocessor implementations offer some flexibility in partitioning by function or data, but on an FPGA, functions are necessarily laid out on the chip and so function-level parallelism is built in although functions can also be replicated for data parallelism.

This implies pipelining not only within, but also across, functions. DNA microarrays simultaneously measure the expression of tens of thousands of genes, and are used to investigate numerous questions in biology.

Fpgas pdf high-performance computing using

One approach is to analyze on the order of a hundred samples, each with tens of thousands of gene expressions, to find correlations between expression patterns and disease phenomena. The kernel operation is a series of dot-product and sum DPS calculations feeding covariance, matrix inversion, and regression CIR logic. Usually the solution involves a very deep pipeline hundreds or even thousands of stages long.

Difficulty arises, however, when successive functions have different rates of sourcing and sinking data. The solution is to rate-match sequential functions by replicating the slower functions and then using them in rotation for the desired throughput. FPGAs are often viewed as homogeneous substrates that can be configured into arbitrary logic.

In the past five years, however, an ever larger fraction of their chip area has been devoted to hard-wired components, such as integer multipliers and independently accessible BRAMs. For example, the Xilinx VP has independently addressable, bit, quad-ported BRAMs; it achieves a sustained bandwidth of 20 terabytes per second at capacity. Using this bandwidth greatly facilitates high performance and is an outstanding asset of current-generation FPGAs.

In molecular dynamics, efficient algorithms for computing the electrostatic interaction often involve mapping charges onto a 3D grid. The first phase of each iteration computes the 3D charge distribution, while the second phase locates each atom in that field and applies a force to it according to its charges and that region of the force field. Because atoms almost never align to the grid points on which the field is computed, trilinear interpolation uses the eight grid points nearest to the atom to determine field strength.

Key to such a structure is simultaneous access to all grid points surrounding the atom. This in turn requires appropriate partitioning of the 3D grid among the BRAMs to enable collisionless access, and also efficient logic to convert atom positions into BRAM addresses. We have prototyped a memory-access configuration that supports tricubic interpolation by fetching 64 neighboring grid-point values per cycle.

Computing pdf fpgas high-performance using

We have also generalized this technique into a tool that creates custom interleaved memories for access kernels of various sizes, shapes, and dimensionality. With high-end microprocessors having bit data paths, often overlooked is that many BCB applications require only a few bits of precision. In fact, even the canonical floating point of MD is often implemented with substantially reduced precision, although this remains controversial.

In contrast with microprocessors, FPGAs enable configuration of data paths into arbitrary sizes, allowing a tradeoff between precision and parallelism. An additional benefit of minimizing precision comes from shorter propagation delays through narrower arithmetic units. All BCB applications described here benefit substantially from the selection of nonstandard data type sizes. For example, microarray values and biological sequences require only two to five bits, and shape characterization of a rigid molecule requires only two to seven bits.

While most MD applications require more than the 24 bits provided by a single-precision floating point, they might not need double precision 53 bits.

We return to the modeling molecular interactions case study to illustrate the tradeoff between PE complexity and degree of parallelism. That study examined six different models describing intermolecular forces. Molecule descriptions range from two to seven bits per voxel, and scoring functions varied with the application. The number of PEs that fit the various maximum-sized cubical computing arrays into a Xilinx XC2VP70 ranged from 8 3 to 2, 14 3 , according to the resources each PE needed.

Since clock speeds also differed for each application-specific accelerator, they covered a 7: If we had been restricted to, for example, 8-bit arithmetic, the performance differential would have been even greater.

Microprocessors provide support for integers and floating point, and, depending on multimedia features, 8-bit saturated values. In digital signal processing systems, however, cost concerns often require DSPs to have only integers. Software can emulate floating point when required; also common is use of block floating point.

Pdf using fpgas high-performance computing

Alternatives include the block floating point, log representations, and the semi-floating point. We would generally use double-precision floating points for further computations.

Careful analysis shows that the number of distinct alignments that must be computed is quite small even though the range of exponents is large. This enables the use of a stripped-down floating-point mode, particularly one that does not require a variable shift.